Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non‐invasive phenotyping

Hitherto, most quantitative trait loci of maize growth and biomass yield have been identified for a single time point, usually the final harvest stage. Through this approach cumulative effects are detected, without considering genetic factors causing phase‐specific differences in growth rates. To assess the genetics of growth dynamics, we employed automated non‐invasive phenotyping to monitor the plant sizes of 252 diverse maize inbred lines at 11 different developmental time points; 50 k SNP array genotype data were used for genome‐wide association mapping and genomic selection. The heritability of biomass was estimated to be over 71%, and the average prediction accuracy amounted to 0.39. Using the individual time point data, 12 main effect marker‐trait associations (MTAs) and six pairs of epistatic interactions were detected that displayed different patterns of expression at various developmental time points. A subset of them also showed significant effects on relative growth rates in different intervals. The detected MTAs jointly explained up to 12% of the total phenotypic variation, decreasing with developmental progression. Using non‐parametric functional mapping and multivariate mapping approaches, four additional marker loci affecting growth dynamics were detected. Our results demonstrate that plant biomass accumulation is a complex trait governed by many small effect loci, most of which act at certain restricted developmental phases. This highlights the need for investigation of stage‐specific growth affecting genes to elucidate important processes operating at different developmental phases.     

Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat

Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.     

Association genetics of complex traits in plants

Association mapping is rapidly becoming the main method for dissecting the genetic architecture of complex traits in plants. Currently most association mapping studies in plants are preformed using sets of genes selected to be putative candidates for the trait of interest, but rapid developments in genomics will allow for genome-wide mapping in virtually any plant species in the near future. As the costs for genotyping are decreasing, the focus has shifted towards phenotyping. In plants, clonal replication and/or inbred lines allows for replicated phenotyping under many different environmental conditions. Reduced sequencing costs will increase the number of studies that use RNA sequencing data to perform expression quantitative trait locus (eQTL) mapping, which will increase our knowledge of how gene expression variation contributes to phenotypic variation. Current population sizes used in association mapping studies are modest in size and need to be greatly increased if mutations explaining less than a few per cent of the phenotypic variation are to be detected. Association mapping has started to yield insights into the genetic architecture of complex traits in plants, and future studies with greater genome coverage will help to elucidate how plants have managed to adapt to a wide variety of environmental conditions.     

Genome‐wide association mapping for female fertility traits in Danish and Swedish Holstein cattle

A genome‐wide association study was conducted using a mixed model analysis for QTL for fertility traits in Danish and Swedish Holstein cattle. The analysis incorporated 2,531 progeny tested bulls, and a total of 36 387 SNP markers on 29 bovine autosomes were used. Eleven fertility traits were analyzed for SNP association. Furthermore, mixed model analysis was used for association analyses where a polygenic effect was fitted as a random effect, and genotypes at single SNPs were successively included as a fixed effect in the model. The Bonferroni correction for multiple testing was applied to adjust the significance threshold. Seventy‐four SNP‐trait combinations showed chromosome‐wide significance, and five of these were significant genome‐wide. Twenty‐four QTL regions on 14 chromosomes were detected. Strong evidence for the presence of QTL that affect fertility traits were observed on chromosomes 3, 5, 10, 13, 19, 20, and 24. The QTL intervals were generally smaller than those described in earlier linkage studies. The identification of fertility trait‐associated SNPs and mapping of the corresponding QTL in small chromosomal regions reported here will facilitate searches for candidate genes and candidate polymorphisms.     

High‐throughput two‐dimensional root system phenotyping platform facilitates genetic analysis of root growth and development

High‐throughput phenotyping of root systems requires a combination of specialized techniques and adaptable plant growth, root imaging and software tools. A custom phenotyping platform was designed to capture images of whole root systems, and novel software tools were developed to process and analyse these images. The platform and its components are adaptable to a wide range root phenotyping studies using diverse growth systems (hydroponics, paper pouches, gel and soil) involving several plant species, including, but not limited to, rice, maize, sorghum, tomato and Arabidopsis. The RootReader2D software tool is free and publicly available and was designed with both user‐guided and automated features that increase flexibility and enhance efficiency when measuring root growth traits from specific roots or entire root systems during large‐scale phenotyping studies. To demonstrate the unique capabilities and high‐throughput capacity of this phenotyping platform for studying root systems, genome‐wide association studies on rice (Oryza sativa) and maize (Zea mays) root growth were performed and root traits related to aluminium (Al) tolerance were analysed on the parents of the maize nested association mapping (NAM) population.     

Confirmation and fine‐mapping of clinical mastitis and somatic cell score QTL in Nordic Holstein cattle

A genome‐wide association study of 2098 progeny‐tested Nordic Holstein bulls genotyped for 36 387 SNPs on 29 autosomes was conducted to confirm and fine‐map quantitative trait loci (QTL) for mastitis traits identified earlier using linkage analysis with sparse microsatellite markers in the same population. We used linear mixed model analysis where a polygenic genetic effect was fitted as a random effect and single SNPs were successively included as fixed effects in the model. We detected 143 SNP‐by‐trait significant associations (P < 0.0001) on 20 chromosomes affecting mastitis‐related traits. Among them, 21 SNP‐by‐trait combinations exceeded the genome‐wide significant threshold. For 12 chromosomes, both the present association study and the previous linkage study detected QTL, and of these, six were in the same chromosomal locations. Strong associations of SNPs with mastitis traits were observed on bovine autosomes 6, 13, 14 and 20. Possible candidate genes for these QTL were identified. Identification of SNPs in linkage disequilibrium with QTL will enable marker‐based selection for mastitis resistance. The candidate genes identified should be further studied to detect candidate polymorphisms underlying these QTL.     

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.     

Genome‐wide association studies for hematological traits in swine

Improving immune capacity may increase the profitability of animal production if it enables animals to better cope with infections. Hematological traits play pivotal roles in animal immune capacity and disease resistance. Thus far, few studies have been conducted using a high‐density swine SNP chip panel to unravel the genetic mechanism of the immune capability in domestic animals. In this study, using mixed model‐based single‐locus regression analyses, we carried out genome‐wide association studies, using the Porcine SNP60 BeadChip, for immune responses in piglets for 18 hematological traits (seven leukocyte traits, seven erythrocyte traits, and four platelet traits) after being immunized with classical swine fever vaccine. After adjusting for multiple testing based on permutations, 10, 24, and 77 chromosome‐wise significant SNPs were identified for the leukocyte traits, erythrocyte traits, and platelet traits respectively, of which 10 reached genome‐wise significance level. Among the 53 SNPs for mean platelet volume, 29 are located in a linkage disequilibrium block between 32.77 and 40.59 Mb on SSC6. Four genes of interest are located within the block, providing genetic evidence that this genomic segment may be considered a candidate region relevant to the platelet traits. Other candidate genes of interest for red blood cell, hemoglobin, and red blood cell volume distribution width also have been found near the significant SNPs. Our genome‐wide association study provides a list of significant SNPs and candidate genes that offer valuable information for future dissection of molecular mechanisms regulating hematological traits.     

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.     

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

In recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High‐throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis‐related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio‐engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype‐to‐gene identification pipeline.     

Association mapping for chilling tolerance in elite flint and dent maize inbred lines evaluated in growth chamber and field experiments

Chilling sensitivity of maize is a strong limitation for its cultivation in the cooler areas of the northern and southern hemisphere because reduced growth in early stages impairs on later biomass accumulation. Efficient breeding for chilling tolerance is hampered by both the complex physiological response of maize to chilling temperatures and the difficulty to accurately measure chilling tolerance in the field under fluctuating climatic conditions. For this research, we used genome‐wide association (GWA) mapping to identify genes underlying chilling tolerance under both controlled and field conditions in a broad germplasm collection of 375 maize inbred lines genotyped with 56 110 single nucleotide polymorphism (SNP). We identified 19 highly significant association signals explaining between 5.7 and 52.5% of the phenotypic variance observed for early growth and chlorophyll fluorescence parameters. The allelic effect of several SNPs identified for early growth was associated with temperature and incident radiation. Candidate genes involved in ethylene signalling, brassinolide, and lignin biosynthesis were found in their vicinity. The frequent involvement of candidate genes into signalling or gene expression regulation underlines the complex response of photosynthetic performance and early growth to climatic conditions, and supports pleiotropism as a major cause of co‐locations of quantitative trait loci for these highly polygenic traits.     

Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand

Stomatal conductance is central for the trades‐off between hydraulics and photosynthesis. We aimed at deciphering its genetic control and that of its responses to evaporative demand and water deficit, a nearly impossible task with gas exchanges measurements. Whole‐plant stomatal conductance was estimated via inversion of the Penman–Monteith equation from data of transpiration and plant architecture collected in a phenotyping platform. We have analysed jointly 4 experiments with contrasting environmental conditions imposed to a panel of 254 maize hybrids. Estimated whole‐plant stomatal conductance closely correlated with gas‐exchange measurements and biomass accumulation rate. Sixteen robust quantitative trait loci (QTLs) were identified by genome wide association studies and co‐located with QTLs of transpiration and biomass. Light, vapour pressure deficit, or soil water potential largely accounted for the differences in allelic effects between experiments, thereby providing strong hypotheses for mechanisms of stomatal control and a way to select relevant candidate genes among the 1–19 genes harboured by QTLs. The combination of allelic effects, as affected by environmental conditions, accounted for the variability of stomatal conductance across a range of hybrids and environmental conditions. This approach may therefore contribute to genetic analysis and prediction of stomatal control in diverse environments.     

Genome‐wide association analysis for growth,muscularity and meat quality in Piétrain pigs

Improvement in growth and meat quality is one of the main objectives in sire line pig breeding programmes. Mapping quantitative trait loci for these traits using experimental crosses and a linkage‐based approach has been performed frequently in the past. The Piétrain breed often was involved as a founder breed to establish the experimental crosses. This breed was selected for muscularity and leanness but shows relatively poor meat quality. It is frequently used as a sire line breed. With the advent of genome‐wide and dense SNP chips in pig genomic research, it is possible to also conduct genome‐wide association studies within the Piétrain breed. In this study, around 500 progeny‐tested sires were genotyped with 60k SNPs. Data filtering showed that around 48k SNPs were useable in this sample. These SNPs were used to conduct a genome‐wide association study for growth, muscularity and meat quality traits. Because it is known that a mutation in the RYR1 gene located on chromosome 6 shows a major effect on meat quality, this mutation was included in the models. Single‐marker and multimarker association analyses were performed. The results revealed between zero and eight significant associations per trait with P < 5 × 10^?5. Of special interest are SNPs located on SSC6, SSC10 and SSC15.     

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.     

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.     

Identification of loci governing eight agronomic traits using a GBS‐GWAS approach and validation by QTL mapping in soya bean

Soya bean is a major source of edible oil and protein for human consumption as well as animal feed. Understanding the genetic basis of different traits in soya bean will provide important insights for improving breeding strategies for this crop. A genome‐wide association study (GWAS) was conducted to accelerate molecular breeding for the improvement of agronomic traits in soya bean. A genotyping‐by‐sequencing (GBS) approach was used to provide dense genome‐wide marker coverage (>47 000 SNPs) for a panel of 304 short‐season soya bean lines. A subset of 139 lines, representative of the diversity among these, was characterized phenotypically for eight traits under six environments (3 sites × 2 years). Marker coverage proved sufficient to ensure highly significant associations between the genes known to control simple traits (flower, hilum and pubescence colour) and flanking SNPs. Between one and eight genomic loci associated with more complex traits (maturity, plant height, seed weight, seed oil and protein) were also identified. Importantly, most of these GWAS loci were located within genomic regions identified by previously reported quantitative trait locus (QTL) for these traits. In some cases, the reported QTLs were also successfully validated by additional QTL mapping in a biparental population. This study demonstrates that integrating GBS and GWAS can be used as a powerful complementary approach to classical biparental mapping for dissecting complex traits in soya bean.     

Relation among plant growth,carbohydrates and flowering time in the Arabidopsis Landsberg erecta × Kondara recombinant inbred line population

Arabidopsis thaliana natural variation was used to study plant performance viewed as the accumulation of photo‐assimilates, their allocation and storage, in relation to other growth‐related features and flowering‐related traits. Quantitative trait locus (QTL) analysis using recombinant inbred lines derived from the cross between Landsberg erecta (originating from Poland) and Kondara (originating from Tajikistan) grown on hydroponics, revealed QTLs for the different aspects of plant growth‐related traits, sugar and starch contents and flowering‐related traits. Co‐locations of QTLs for these different aspects were detected at different regions, mainly at the ER locus; the top of chromosomes 3, 4 and 5; and the bottom of chromosome 5. Increased plant growth was associated with early flowering and leaf transitory starch, and correlated negatively with the levels of soluble sugar at early phases of development. From the significant correlations and the co‐locations of the QTLs for these aspects, we conclude that there is a complex relationship between plant growth‐related traits, carbohydrate content and flowering‐related traits.