Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high‐throughput phenotypic analysis
Ying ZhangJinglu WangJianjun DuYanxin ZhaoXianju LuWeiliang WenShenghao GuJiangchuan FanChuanyu WangSheng WuYongjian WangShengjin LiaoChunjiang ZhaoXinyu Guo
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Summary High‐throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time‐consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at high‐throughput are urgently needed for large crop populations. In this study, we developed a standard process for stem micro‐CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems including geometry‐related, morphology‐related and distribution‐related traits. Next, we analysed the phenotypic variation of stem vascular bundles between natural population subgroup (480 inbred lines) based on 48 comprehensively phenotypic information. Also, the first database for stem micro‐phenotypes, MaizeSPD, was established, storing 554 pieces of basic information of maize inbred lines, 523 pieces of experimental information, 1008 pieces of CT scanning images and processed images, and 24 192 pieces of phenotypic data. Combined with genome‐wide association studies (GWASs), a total of 1562 significant single nucleotide polymorphism (SNPs) were identified for 30 stem micro‐phenotypic traits, and 84 unique genes of 20 traits such as VBNum, VBAvArea and PZVBDensity were detected. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to plant signal transduction and stress response. The results presented here will advance our knowledge about phenotypic trait components of stem vascular bundles and provide useful information for understanding the genetic controls of vascular bundle formation and development.Keywords:
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Abstract The rapeseed branch angle is an important morphological trait because an adequate branch angle enables more efficient light capture under high planting densities. Here, we report that the average angle of the five top branches provides a reliable representation of the average angle of all branches. Statistical analyses revealed a significantly positive correlation between the branch angle and multiple plant-type and yield-related traits. The 60 K Brassica Infinium ® single nucleotide polymorphism (SNP) array was utilized to genotype an association panel with 520 diverse accessions. A genome-wide association study was performed to determine the genetic architecture of branch angle and 56 loci were identified as being significantly associated with the branch angle trait via three models, including a robust, novel, nonparametric Anderson-Darling (A-D) test. Moreover, these loci explained 51.1% of the phenotypic variation when a simple additive model was applied. Within the linkage disequilibrium (LD) decay ranges of 53 loci, we observed plausible candidates orthologous to documented Arabidopsis genes, such as LAZY1 , SGR2 , SGR4 , SGR8 , SGR9 , PIN3 , PIN7 , CRK5 , TIR1 and APD7 . These results provide insight into the genetic basis of the branch angle trait in rapeseed and might facilitate marker-based breeding for improvements in plant architecture.
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Abstract One of the main outcome of quantitative genetics approaches to natural variation is to reveal the genetic architecture underlying the phenotypic space. Complex genetic architectures are described as including numerous loci (or alleles) with small-effect and/or low-frequency in the populations, interactions with the genetic background, environment or age… Linkage or association mapping strategies will be more or less sensitive to this complexity, so that we still have an unclear picture of its extent. By combining high-throughput phenotyping under two environmental conditions with classical QTL mapping approaches in multiple Arabidopsis thaliana segregating populations as well as advanced near isogenic lines construction and survey, we have attempted to push back the limits of our understanding of quantitative phenotypic variation. Integrative traits such as those related to vegetative growth used in this work (highlighting either cumulative growth, growth rate or morphology) all showed complex and dynamic genetic architecture with respect to the segregating population and condition. The more resolutive our mapping approach, the more complexity we uncover, with several instances of QTLs visible in near isogenic lines but not detected with the initial QTL mapping, indicating that our phenotyping resolution was less limiting than the mapping resolution with respect to the underlying genetic architecture. In an ultimate approach to resolve this complexity, we intensified our phenotyping effort to target specifically a 3Mb-region known to segregate for a major quantitative trait gene, using a series of selected lines recombined every 100kb. We discovered that at least 3 other independent QTLs had remained hidden in this region, some with trait- or condition-specific effects, or opposite allelic effects. If we were to extrapolate the figures obtained on this specific region in this particular cross to the genome- and species-scale, we would predict hundreds of causative loci of detectable phenotypic effect controlling these growth-related phenotypes.
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One of the main outcomes of quantitative genetics approaches to natural variation is to reveal the genetic architecture underlying the phenotypic space. Complex genetic architectures are described as including numerous loci (or alleles) with small-effect and/or low-frequency in the populations, interactions with the genetic background, environment or age. Linkage or association mapping strategies will be more or less sensitive to this complexity, so that we still have an unclear picture of its extent. By combining high-throughput phenotyping under two environmental conditions with classical QTL mapping approaches in multiple Arabidopsis thaliana segregating populations as well as advanced near isogenic lines construction and survey, we have attempted to improve our understanding of quantitative phenotypic variation. Integrative traits such as those related to vegetative growth used in this work (highlighting either cumulative growth, growth rate or morphology) all showed complex and dynamic genetic architecture with respect to the segregating population and condition. The more resolutive our mapping approach, the more complexity we uncover, with several instances of QTLs visible in near isogenic lines but not detected with the initial QTL mapping, indicating that our phenotyping accuracy was less limiting than the mapping resolution with respect to the underlying genetic architecture. In an ultimate approach to resolve this complexity, we intensified our phenotyping effort to target specifically a 3Mb-region known to segregate for a major quantitative trait gene, using a series of selected lines recombined every 100kb. We discovered that at least 3 other independent QTLs had remained hidden in this region, some with trait- or condition-specific effects, or opposite allelic effects. If we were to extrapolate the figures obtained on this specific region in this particular cross to the genome- and species-scale, we would predict hundreds of causative loci of detectable phenotypic effect controlling these growth-related phenotypes.
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Understanding and estimating the structure and parameters associated with the genetic architecture of quantitative traits is a major research focus in quantitative genetics. With the availability of a well-saturated genetic map of molecular markers, it is possible to identify a major part of the structure of the genetic architecture of quantitative traits and to estimate the associated parameters. Multiple interval mapping, which was recently proposed for simultaneously mapping multiple quantitative trait loci (QTL), is well suited to the identification and estimation of the genetic architecture parameters, including the number, genomic positions, effects and interactions of significant QTL and their contribution to the genetic variance. With multiple traits and multiple environments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment interactions can also be estimated. We review the method and discuss issues associated with multiple interval mapping, such as likelihood analysis, model selection, stopping rules and parameter estimation. The potential power and advantages of the method for mapping multiple QTL and estimating the genetic architecture are discussed. We also point out potential problems and difficulties in resolving the details of the genetic architecture as well as other areas that require further investigation. One application of the analysis is to improve genome-wide marker-assisted selection, particularly when the information about epistasis is used for selection with mating.
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Abstract Modifying plant architecture is often necessary for yield improvement and climate adaptation, but we lack understanding of the genotype-phenotype map for plant morphology in sorghum. Here, we use a nested association mapping (NAM) population that captures global allelic diversity of sorghum to characterize the genetics of leaf erectness, leaf width (at two stages), and stem diameter. Recombinant inbred lines (n = 2200) were phenotyped in multiple environments (35,200 observations) and joint linkage mapping was performed with ∼93,000 markers. Fifty-four QTL of small to large effect were identified for trait BLUPs (9–16 per trait) each explaining 0.4–4% of variation across the NAM population. While some of these QTL colocalize with sorghum homologs of grass genes [e.g. involved in hormone synthesis (maize spi1 ), floral transition ( SbCN8 ), and transcriptional regulation of development (rice Ideal plant architecture1 )], most QTL did not colocalize with an a priori candidate gene (82%). Genomic prediction accuracy was generally high in five-fold cross-validation (0.65–0.83), and varied from low to high in leave-one-family-out cross-validation (0.04–0.61). The findings provide a foundation to identify the molecular basis of architecture variation in sorghum and establish genomic-enabled breeding for improved plant architecture. Core ideas Understanding the genetics of plant architecture could facilitate the development of crop ideotypes for yield and adaptation The genetics of plant architecture traits was characterized in sorghum using multi-environment phenotyping in a global nested association mapping population Fifty-five quantitative trait loci were identified; some colocalize with homologs of known developmental regulators but most do not Genomic prediction accuracy was consistently high in five-fold cross-validation, but accuracy varied considerably in leave-one-family-out predictions
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Barley, globally the fourth most important cereal, provides food and beverages for humans and feed for animal husbandry. Maximizing grain yield under varying climate conditions largely depends on the optimal timing of flowering. Therefore, regulation of flowering time is of extraordinary importance to meet future food and feed demands. We developed the first barley nested association mapping (NAM) population, HEB-25, by crossing 25 wild barleys with one elite barley cultivar, and used it to dissect the genetic architecture of flowering time.Upon cultivation of 1,420 lines in multi-field trials and applying a genome-wide association study, eight major quantitative trait loci (QTL) were identified as main determinants to control flowering time in barley. These QTL accounted for 64% of the cross-validated proportion of explained genotypic variance (pG). The strongest single QTL effect corresponded to the known photoperiod response gene Ppd-H1. After sequencing the causative part of Ppd-H1, we differentiated twelve haplotypes in HEB-25, whereof the strongest exotic haplotype accelerated flowering time by 11 days compared to the elite barley haplotype. Applying a whole genome prediction model including main effects and epistatic interactions allowed predicting flowering time with an unmatched accuracy of 77% of cross-validated pG.The elaborated causal models represent a fundamental step to explain flowering time in barley. In addition, our study confirms that the exotic biodiversity present in HEB-25 is a valuable toolbox to dissect the genetic architecture of important agronomic traits and to replenish the elite barley breeding pool with favorable, trait-improving exotic alleles.
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The house mouse is one of the most successful mammals and the premier research animal in mammalian biology. The classical inbred strains of house mice have been artificially modified to facilitate identification of the genetic factors underlying phenotypic variation among these strains. Despite their widespread use in basic and biomedical research, functional and evolutionary morphologists have not taken full advantage of inbred mice as a model for studying the genetic architecture of form, function, and performance in mammals. We illustrate the potential of inbred mice as a model for mammalian functional morphology by examining the genetic architecture of maximum jaw-opening performance, or maximum gape, across 21 classical inbred strains. We find that variation in maximum gape among these strains is heritable, providing the first evidence of a genetic contribution to maximum jaw-opening performance in mammals. Maximum gape exhibits a significant genetic correlation with body size across strains, raising the possibility that evolutionary increases in size frequently resulted in correlated increases in maximum gape (within the constraints of existing craniofacial form) during mammalian evolution. Several craniofacial features that influence maximum gape share significant phenotypic and genetic correlations with jaw-opening ability across these inbred strains. The significant genetic correlations indicate the potential for coordinated evolution of craniofacial form and jaw-opening performance, as hypothesized in several comparative analyses of mammals linking skull form to variation in jaw-opening ability. Functional studies of mammalian locomotion and feeding have only rarely examined the genetic basis of functional and performance traits. The classical inbred strains of house mice offer a powerful tool for exploring this genetic architecture and furthering our understanding of how form, function, and performance have evolved in mammals.
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Abstract Recent genome-wide association studies suggest that the human genetic architecture of complex traits may vary between males and females; however, traditional approaches for association mapping cannot fully account for these between-sex differences... Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. However, current association testing approaches for dealing with sex, such as including sex as a covariate, cannot fully account for these phenomena and can be suboptimal in statistical power. We present a novel association mapping framework, MetaSex, that can comprehensively account for the genetic architecture differences between the sexes. Through simulations and applications to real data, we show that our framework has superior performance than previous approaches in association mapping.
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Information on the genetic architecture of phenotypic traits is helpful for constructing and testing models of the ecoevolutionary dynamics of natural populations. For plant groups with long life cycles there is a lack of line cross experiments that can unravel the genetic architecture of loci underlying quantitative traits. To fill this gap, we propose the use of variation for phenotypic traits expressed in natural hybrid zones as an alternative approach. We used data from orchid hybrid zones and compared expected and observed patterns of phenotypic trait expression in different early-generation hybrid classes identified by molecular genetic markers. We found evidence of additivity, dominance, and epistatic interactions for different phenotypic traits. We discuss the potential of this approach along with its limitations and suggest that it may represent a realistic way to gain an initial insight into the heritability and genomic architecture of traits in organismal groups with complex life history, such as orchids and many others.
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