Association Mapping Population Research Articles

Genomic prediction in Brassica napus: evaluating the benefit of imputed whole-genome sequencing data.

Advances in sequencing technology allow whole plant genomes to be sequenced with high quality. Combining genotypic and phenotypic data in genomic prediction helps breeders to select crossing partners in partially phenotyped populations. In plant breeding programs, the cost of sequencing entire breeding populations still exceeds available genotyping budgets. Hence, the method for genotyping is still mainly single nucleotide polymorphism (SNP) arrays; however, arrays are unable to assess the entire genome- and population-wide diversity. A compromise involves genotyping the entire population using an SNP array and a subset of the population with whole-genome sequencing. Both datasets can then be used to impute markers from whole-genome sequencing onto the entire population. Here, we evaluate whether imputation of whole-genome sequencing data enhances genomic predictions, using data from a nested association mapping population of rapeseed (Brassica napus). Employing two cross-validation schemes that mimic scenarios for the prediction of close and distant relatives, we show that imputed marker data do not significantly improve prediction accuracy, likely due to redundancy in relationship estimates and imputation errors. In simulation studies, only small improvements were observed, further corroborating the findings. We conclude that SNP arrays are already equipped with the information that is added by imputation through relationship and linkage disequilibrium.

Investigating the genetic control of plant development in spring barley under speed breeding conditions

Key messageThis study found that the genes, PPD-H1 and ELF3, control the acceleration of plant development under speed breeding, with important implications for optimizing the delivery of climate-resilient crops.Speed breeding is a tool to accelerate breeding and research programmes. Despite its success and growing popularity with breeders, the genetic basis of plant development under speed breeding remains unknown. This study explored the developmental advancements of barley genotypes under different photoperiod regimes. A subset of the HEB-25 Nested Association Mapping population was evaluated for days to heading and maturity under two contrasting photoperiod conditions: (1) Speed breeding (SB) consisting of 22 h of light and 2 h of darkness, and (2) normal breeding (NB) consisting of 16 h of light and 8 h of darkness. GWAS revealed that developmental responses under both conditions were largely controlled by two loci: PPDH-1 and ELF3. Allelic variants at these genes determine whether plants display early flowering and maturity under both conditions. At key QTL regions, domesticated alleles were associated with late flowering and maturity in NB and early flowering and maturity in SB, whereas wild alleles were associated with early flowering under both conditions. We hypothesize that this is related to the dark-dependent repression of PPD-H1 by ELF3 which might be more prominent in NB conditions. Furthermore, by comparing development under two photoperiod regimes, we derived an estimate of plasticity for the two traits. Interestingly, plasticity in development was largely attributed to allelic variation at ELF3. Our results have important implications for our understanding and optimization of speed breeding protocols particularly for introgression breeding and the design of breeding programmes to support the delivery of climate-resilient crops.

Linkage-Mapping and Genome-wide Association Study Identified Two Peanut Late Leaf Spot Resistance Loci, PLLSR-1 and PLLSR-2, Using a Nested Association Mapping.

Identification of candidate genes and molecular markers for late leaf spot (LLS) disease resistance in peanut (Arachis hypogaea) has been a focus of molecular breeding for the U.S. industry funded peanut genome project. Efforts have been hindered by limited mapping resolution due to low levels of genetic recombination and marker density available in traditional biparental mapping populations. To address this, a multi-parental nested association mapping (NAM) population has been genotyped with the peanut 58 K SNP array and phenotyped for LLS severity in the field for three years. Joint linkage-based QTL mapping identified nine QTLs for LLS resistance with significant phenotypic variance explained (PVE) up to 47.7%. A genome-wide association study (GWAS) identified 13 SNPs consistently associated with LLS resistance. Two genomic regions harboring the consistent QTLs and SNPs were identified from 1,336 Kb to 1,520 Kb (184 Kb) on chromosome B02 and from 1,026.9 Kb to 1,793.2 Kb (767 Kb) on chromosome B03, designated as peanut late leaf spot resistance loci, PLLSR-1 and PLLSR-2, respectively. PLLSR-1 contains 10 NBS-LRR disease resistant genes. An NBS-LRR disease resistance gene Arahy.VKVT6A was also identified on homoeologous chromosome A02. PLLSR-2 contains five significant SNPs associated with five different genes encoding callose synthase, pollen defective in guidance protein, pentatricopeptide repeat (PPR), acyl-activating enzyme, and C2 GRAM domains-containing protein. This study highlights the power of multi-parent populations such as NAM for genetic mapping and marker-trait association studies in peanuts. Validation of these two LLS resistance loci will be needed for marker-assisted breeding.

Evaluating and Predicting the Performance of Sorghum Lines in an Elite by Exotic Backcross-Nested Association Mapping Population.

Maintaining or introducing genetic diversity into plant breeding programs is necessary for continual genetic gain; however, diversity at the cost of reduced performance is not something sought by breeders. To this end, backcross-nested association mapping (BC-NAM) populations, in which the recurrent parent is an elite line, can be employed as a strategy to introgress diversity from unadapted accessions while maintaining agronomic performance. This study evaluates (i) the hybrid performance of sorghum lines from 18 BC1-NAM families and (ii) the potential of genomic prediction to screen lines from BC1-NAM families for hybrid performance prior to phenotypic evaluation. Despite the diverse geographical origins and agronomic performance of the unadapted parents for BC1-NAM families, many BC1-derived lines performed significantly better in the hybrid trials than the elite recurrent parent, R.Tx436. The genomic prediction accuracies for grain yield, plant height, and days to mid-anthesis were acceptable, but the prediction accuracies for plant height were lower than expected. While the prediction accuracies increased when including more individuals in the training set, improvements tended to plateau between two and five lines per family, with larger training sets being required for more complex traits such as grain yield. Therefore, genomic prediction models can be optimized in a large BC1-NAM population with a relatively low fraction of individuals needing to be evaluated. These results suggest that genomic prediction is an effective method of pre-screening lines within BC1-NAM families prior to evaluation in extensive hybrid field trials.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits.

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

A new winter wheat genetic resource harbors untapped diversity from synthetic hexaploid wheat

Key messageThe NIAB_WW_SHW_NAM population, a large nested association mapping panel, is a useful resource for mapping QTL from synthetic hexaploid wheat that can improve modern elite wheat cultivars.The allelic richness harbored in progenitors of hexaploid bread wheat (Triticum aestivum L.) is a useful resource for addressing the genetic diversity bottleneck in modern cultivars. Synthetic hexaploid wheat (SHW) is created through resynthesis of the hybridisation events between the tetraploid (Triticum turgidum subsp. durum Desf.) and diploid (Aegilops tauschii Coss.) bread wheat progenitors. We developed a large and diverse winter wheat nested association mapping (NAM) population (termed the NIAB_WW_SHW_NAM) consisting of 3241 genotypes derived from 54 nested back-cross 1 (BC1) populations, each formed via back-crossing a different primary SHW into the UK winter wheat cultivar ‘Robigus’. The primary SHW lines were created using 15 T. durum donors and 47 Ae. tauschii accessions that spanned the lineages and geographical range of the species. Primary SHW parents were typically earlier flowering, taller and showed better resistance to yellow rust infection (Yr) than ‘Robigus’. The NIAB_WW_SHW_NAM population was genotyped using a single nucleotide polymorphism (SNP) array and 27 quantitative trait loci (QTLs) were detected for flowering time, plant height and Yr resistance. Across multiple field trials, a QTL for Yr resistance was found on chromosome 4D that corresponded to the Yr28 resistance gene previously reported in other SHW lines. These results demonstrate the value of the NIAB_WW_SHW_NAM population for genetic mapping and provide the first evidence of Yr28 working in current UK environments and genetic backgrounds. These examples, coupled with the evidence of commercial wheat breeders selecting promising genotypes, highlight the potential value of the NIAB_WW_SHW_NAM to variety improvement.

Characterization of adaptation mechanisms in sorghum using a multireference back-cross nested association mapping design and envirotyping.

Identifying the genetic factors impacting the adaptation of crops to environmental conditions is of key interest for conservation and selection purposes. It can be achieved using population genomics, and evolutionary or quantitative genetics. Here we present a sorghum multireference back-cross nested association mapping population composed of 3,901 lines produced by crossing 24 diverse parents to 3 elite parents from West and Central Africa-back-cross nested association mapping. The population was phenotyped in environments characterized by differences in photoperiod, rainfall pattern, temperature levels, and soil fertility. To integrate the multiparental and multi-environmental dimension of our data we proposed a new approach for quantitative trait loci (QTL) detection and parental effect estimation. We extended our model to estimate QTL effect sensitivity to environmental covariates, which facilitated the integration of envirotyping data. Our models allowed spatial projections of the QTL effects in agro-ecologies of interest. We utilized this strategy to analyze the genetic architecture of flowering time and plant height, which represents key adaptation mechanisms in environments like West Africa. Our results allowed a better characterization of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod-sensitive and the region of possible candidate gene Elf3 being photoperiod-insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology-dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the West and Central Africa-back-cross nested association mapping and the presented analytical approach constitute unique resources to better understand adaptation in sorghum with direct application to develop climate-smart varieties.

A wild barley nested association mapping population shows a wide variation for yield-associated traits to be used for breeding in Australian environment

This study aimed to identify wild barley alleles controlling grain size and weight with the potential to improve barley yield in Australia and worldwide. The HEB-25 nested association mapping population was used, which samples 25 different wild barley accessions in a ‘Barke’ genetic background. The HEB-25 population was evaluated in field conditions at Strathalbyn in South Australia in 2015 and 2016. Seven yield component traits reflecting ear length, grain number per ear and grain dimension were measured. Among 114 quantitative trait loci (QTL) identified for the seven traits in both years, many co-localise with known genes controlling flowering and spike morphology. There were 18 QTL hotspots associated with four loci or more, of which one at the beginning of chromosome 5H had wild alleles that increased both grain number per ear and thousand-grain weight. A wide range of effects was found for wild alleles for each trait across all QTL identified, providing a rich source of genetic diversity that barley breeders can exploit to enhance barley yield.

Identification of Novel Quantitative Trait Loci Associated with Table Grape Fruit Quality Characteristics in a Segregating Population and Transferability of Existing Quality Markers

Grape (Vitis) production and fruit quality traits such as cluster size, berry shape, and timing of fruit development are key aspects when selecting cultivars for commercial production. Molecular markers for some, but not all, of these traits have been identified using biparental or association mapping populations. Previously identified markers were tested for transferability using a small (24 individual) test panel of commercially available grape cultivars. Markers had little to no ability to differentiate grape phenotypes based on the expected characteristics, except the marker for seedlessness. Using a biparental interspecific cross, 43 quantitative trait loci (QTLs) (previously identified and new genomic regions) associated with berry shape, number, size, cluster weight, cluster length, time to flower, veraison, and full color were detected. Kompetitive allele-specific polymerase chain reaction markers designed on newly identified QTLs were tested for transferability using the same panel. Transferability was low when use types were combined, but they were varied when use types were evaluated separately. A comparison of a 4-Mb region at the end of chromosome 18 revealed structural differences among grape species and use types. Table grape cultivars had the highest similarity in structure for this region (>75%) compared with other grape species and commodity types.

Registration of sorghum backcross‐nested association mapping (BC‐NAM) families in a BTx623 or RTx436 background

AbstractTwo sorghum [Sorghum bicolor (L.) Moench] backcross‐nested association mapping (BC‐NAM) populations (Reg. no. MP‐5, NSL 546724 MAP) have been developed, composed of 30 families totaling 2189 BC1F4 lines using an elite maintainer (B) or restorer (R) line as the recurrent parent. The families were derived from unadapted founder lines that were backcrossed to BTx623 and/or RTx436 based on the fertility reaction score and phylogenetic analysis of the founder lines. These unadapted founder lines were selected based on agronomic fitness, breeder desirability, and genetic diversity and represent a range of races and working groups. The 30 families were developed from 27 founder parents—three of the founder parents were backcrossed to both recurrent parents to provide a genetic relationship across all 30 families. Each family consists of 45–100 BC1F4 lines selected for standard agronomic fitness, and each population was genotyped using genotype‐by‐sequencing. To identify families that possess (or segregate for) traits of interest, 13 lines from each population were randomly selected and phenotyped for a range of agronomic and stress tolerance traits. These BC‐NAM populations, which are adapted to US production regions and possess novel genetic diversity, have application in genetic research as a mapping resource for traits of interest and for sorghum breeding programs to diversify R and B line parental heterotic pools.

PRESENT STATUS AND FUTURE PERSPECTIVES OF WHEAT (TRITICUM AESTIVUM L.) RESEARCH IN UZBEKISTAN

Wheat (Triticum spp.) is one of the premier staple foods consumed by one-third of the world’s population. Bread wheat (Triticum aestivum L.) is the only allohexaploid species with a genome formula BBAADD. Until now, from a selection point of view, a decline showed in the genetic potential of this type of wheat. However, the solution to this problem can be by developing high-yielding, disease- and pest-resistant cultivars using wheat-related species, ancient local landraces, and germplasm resources. Therefore, extensive study of the wheat gene pool using molecular tools, including identifying primary sources, is highly beneficial. For wheat improvement, breeding opportunities offer significant enhancements via genetic mapping approaches. The review focuses on the common wheat germplasm, wheat genetic approaches for genetic mapping, identification, and RWA (Russian wheat aphid) resistance, nested association mapping (NAM) population, DNA barcoding of Uzbekistan elite bread wheat cultivars, DNA marker-based screening of wheat germplasm for RWA resistance, future perspectives of wheat breeding in Uzbekistan, marker-assisted selection for abiotic stress tolerance, wheat stripe rust and its control strategies in Uzbekistan, epidemiology of the rust pathogens, pathogen characterization, and varietal resistance.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Enchanting Insights and Exploring the Relevance of the Magic Population in Advancing Cereal Crop Improvement

Multiparent Advanced Generation Inter-cross Populations (MAGIC) populations are portrayed as inbred recombinant sets that were created by inter-crossing certain numerous parents. However it takes a lot of work to produce MAGIC populations but Its more effective approach for detecting QTL’s (Quantitative Trait Loci) when we involves MAGIC populations. The availability of more genetic variety in the germplasms due to various founder lines is one of the main benefits of employing a MAGIC population. In order to identify QTL’s for crop improvement under biotic and abiotic stress conditions, MAGIC populations were developed for the indica and japonica rice cultivars as well as MAGIC plus and Global MAGIC populations. Also the employment of MAGIC populations for the improvement of rice grain size also improves the yield and quality aspects of rice. Utilization of rice MAGIC plus populations for the improvement of nutrient profile of rice which is prime important in the biofortification program of rice as a goal of united nations. Where the utilization of association mapping and MAGIC populations in maize crop for improving saccharification efficiency results in higher biofuel production. Various institutes and universities are involved in production of MAGIC populations in respective specialised crops. This article gives the idea of MAGIC populations and their applications in different and diverse applications for the ultimate benefit of yield improvement.

Multi-parental fungal mapping population study to detect genomic regions associated with Pyrenophora teres f. teres virulence

In recent years multi-parental mapping populations (MPPs) have been widely adopted in many crops to detect quantitative trait loci (QTLs) as this method can compensate for the limitations of QTL analyses using bi-parental mapping populations. Here we report the first multi-parental nested association mapping (MP-NAM) population study used to detect genomic regions associated with host-pathogenic interactions. MP-NAM QTL analyses were conducted on 399 Pyrenophora teres f. teres individuals using biallelic, cross-specific and parental QTL effect models. A bi-parental QTL mapping study was also conducted to compare the power of QTL detection between bi-parental and MP-NAM populations. Using MP-NAM with 399 individuals detected a maximum of eight QTLs with a single QTL effect model whilst only a maximum of five QTLs were detected with an individual bi-parental mapping population of 100 individuals. When reducing the number of isolates in the MP-NAM to 200 individuals the number of QTLs detected remained the same for the MP-NAM population. This study confirms that MPPs such as MP-NAM populations can be successfully used in detecting QTLs in haploid fungal pathogens and that the power of QTL detection with MPPs is greater than with bi-parental mapping populations.

Comparison of Various Drought Resistance Traits in Soybean (Glycine max L.) Based on Image Analysis for Precision Agriculture.

Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is considered the most efficient method for crop producers. To accelerate breeding cycles, genetic engineering and high-throughput phenotyping technologies have replaced conventional breeding methods. However, the current novel phenotyping method still needs to be optimized by species and varieties. Therefore, we aimed to assess the most appropriate and effective phenotypes for evaluating drought stress by applying a high-throughput image-based method on the nested association mapping (NAM) population of soybeans. The acquired image-based traits from the phenotyping platform were divided into three large categories-area, boundary, and color-and demonstrated an aspect for each characteristic. Analysis on categorized traits interpreted stress responses in morphological and physiological changes. The evaluation of drought stress regardless of varieties was possible by combining various image-based traits. We might suggest that a combination of image-based traits obtained using computer vision can be more efficient than using only one trait for the precision agriculture.

Dissecting genetic loci of yield, yield components, and protein content in bread wheat nested association mapping population

Dissecting genetic loci of yield, yield components, and protein content in bread wheat nested association mapping population

Genomics and phenomics enabled prebreeding improved early-season chilling tolerance in Sorghum.

In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting, and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index values from the chilling nested association mapping population detected CT quantitative trait locus (QTL) that colocalized with manual phenotyping CT QTL. Two of the 4 first-generation Kompetitive Allele Specific PCR (KASP) molecular markers, generated using the peak QTL single nucleotide polymorphisms (SNPs), failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic fixation index analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from 2 independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13-24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.

Detecting directional and non-directional epistasis in bi-parental populations using genomic data.

Epistasis, commonly defined as interaction effects between alleles of different loci, is an important genetic component of the variation of phenotypic traits in natural and breeding populations. In addition to its impact on variance, epistasis can also affect the expected performance of a population and is then referred to as directional epistasis. Before the advent of genomic data, the existence of epistasis (both directional and non-directional) was investigated based on complex and expensive mating schemes involving several generations evaluated for a trait of interest. In this study, we propose a methodology to detect the presence of epistasis based on simple inbred biparental populations, both genotyped and phenotyped, ideally along with their parents. Thanks to genomic data, parental proportions as well as shared parental proportions between inbred individuals can be estimated. They allow the evaluation of epistasis through a test of the expected performance for directional epistasis or the variance of genetic values. This methodology was applied to two large multiparental populations, i.e. the American maize and soybean nested association mapping populations, evaluated for different traits. Results showed significant epistasis, especially for the test of directional epistasis, e.g. the increase in anthesis to silking interval observed in most maize inbred progenies or the decrease in grain yield observed in several soybean inbred progenies. In general, the effects detected suggested that shuffling allelic associations of both elite parents had a detrimental effect on the performance of their progeny. This methodology is implemented in the EpiTest R-package and can be applied to any bi/multiparental inbred population evaluated for a trait of interest.

Nitrogen uptake and remobilization from pre- and post-anthesis stages contribute towards grain yield and grain protein concentration in wheat grown in limited nitrogen conditions

BackgroundIn wheat, nitrogen (N) remobilization from vegetative tissues to developing grains largely depends on genetic and environmental factors. The evaluation of genetic potential of crops under limited resource inputs such as limited N supply would provide an opportunity to identify N-efficient lines with improved N utilisation efficiency and yield potential. We assessed the genetic variation in wheat recombinant inbred lines (RILs) for uptake, partitioning, and remobilization of N towards grain, its association with grain protein concentration (GPC) and grain yield.MethodsWe used the nested association mapping (NAM) population (195 lines) derived by crossing Paragon (P) with CIMMYT core germplasm (P × Cim), Baj (P × Baj), Watkins (P × Wat), and Wyalkatchem (P × Wya). These lines were evaluated in the field for two seasons under limited N supply. The plant sampling was done at anthesis and physiological maturity stages. Various physiological traits were recorded and total N uptake and other N related indices were calculated. The grain protein deviation (GPD) was calculated from the regression of grain yield on GPC. These lines were grouped into different clusters by hierarchical cluster analysis based on grain yield and N-remobilization efficiency (NRE).ResultsThe genetic variation in accumulation of biomass at both pre- and post-anthesis stages were correlated with grain-yield. The NRE significantly correlated with aboveground N uptake at anthesis (AGNa) and grain yield but negatively associated with AGN at post-anthesis (AGNpa) suggesting higher N uptake till anthesis favours high N remobilization during grain filling. Hierarchical cluster analysis of these RILs based on NRE and yield resulted in four clusters, efficient (31), moderately efficient (59), moderately inefficient (58), and inefficient (47). In the N-efficient lines, AGNa contributed to 77% of total N accumulated in grains, while it was 63% in N-inefficient lines. Several N-efficient lines also exhibited positive grain protein deviation (GPD), combining high grain yield and GPC. Among crosses, the P × Cim were superior and N-efficient, while P × Wya responded poorly to low N input.ConclusionsWe propose that traits favouring pre- or post-anthesis biomass accumulation and pre-anthesis N uptake may be targeted for breeding to improve grain-yield under limited N. The lines with positive GPD, a first report of genotype-dependent GPD associated with both AGNpa and AGNa in wheat, may be used as varieties or genetic resources to improve grain yield with high GPC for sustainable development under limited N conditions.

Novel exotic alleles of EARLY FLOWERING 3 determine plant development in barley.

EARLY FLOWERING 3 (ELF3) is an important regulator of various physiological and developmental processes and hence may serve to improve plant adaptation which will be substantial for future plant breeding. To expand the limited knowledge on barley ELF3 in determining agronomic traits, we conducted field studies with heterogeneous inbred families (HIFs) derived from selected lines of the wild barley nested association mapping population HEB-25. During two growing seasons, phenotypes of nearly isogenic HIF sister lines, segregating for exotic and cultivated alleles at the ELF3 locus, were compared for ten developmental and yield-related traits. We determine novel exotic ELF3 alleles and show that HIF lines, carrying the exotic ELF3 allele, accelerated plant development compared to the cultivated ELF3 allele, depending on the genetic background. Remarkably, the most extreme effects on phenology could be attributed to one exotic ELF3 allele differing from the cultivated Barke ELF3 allele in only one SNP. This SNP causes an amino acid substitution (W669G), which predictively has an impact on the protein structure of ELF3, thereby possibly affecting phase separation behaviour and nano-compartment formation of ELF3 and, potentially, also affecting its local cellular interactions causing significant trait differences between HIF sister lines.