QTL analysis of sorghum grain traits based on high-density genetic map.

Sorghum grain traits are important agronomic traits directly related to yield and are key factors affecting the brewing process of distill liquor. Exploring the genes controlling those traits is of great significance for understanding the genetic mechanism of sorghum grain development. In this study, we conducted genotyping using Super-GBS technology on a recombinant inbred lines (RILs) population derived from the cross between "BTx623" and "Hongyingzi," consisting of 205 lines. The grain-related traits of the RIL population were investigated in Guiyang, Anshun in Guizhou, and Ledong in Hainan in China. By inclusive composite interval mapping (ICIM) method, a total of 47 quantitative trait locus (QTL) related to four grain traits (thousand grain weight, grain length, grain width, and length-width ratio) were identified across 10 chromosomes. Among them, 20 important QTL were repeatedly detected in multiple traits or environments and distributed on chromosomes 1 (1), 2 (2), 3 (5), 4 (5), 5 (1), 6 (2), 7 (2), 8 (1), and 9 (1). Six candidate genes were identified within the confidence interval of these QTL, and they are homologous to genes controlling rice grain development (OsMADS1, RGG2, OsNST1, SMG1, OsGRF8, and OsAP2-39). The results provide a basis for further cloning and functional verification of these candidate genes.

Alleles on locus chromosome 4B from different parents confer tiller number and the yield-associated traits in wheat.

Pleiotropy is frequently detected in agronomic traits of wheat (Triticum aestivum). A locus on chromosome 4B, QTn/Ptn/Sl/Sns/Al/Tgw/Gl/Gw.caas-4B, proved to show pleiotropic effects on tiller, spike, and grain traits using a recombinant inbred line (RIL) population of Qingxinmai × 041133. The allele from Qingxinmai increased tiller numbers, and the allele from line 041133 produced better performances of spike traits and grain traits. Another 52 QTL for the eight traits investigated were detected on 18 chromosomes, except for chromosomes 5D, 6D, and 7B. Several genes in the genomic interval of the locus on chromosome 4B were differentially expressed in crown and inflorescence samples between Qingxinmai and line 041133. The development of the KASP marker specific for the locus on chromosome 4B is useful for molecular marker-assisted selection in wheat breeding.

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified Using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers.

This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint-strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]-High-Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica, respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and QTLs for grain length, co-detected by all GAPIT models on chromosomes (Chr) 1-3, 5, 7, and 11, were qGL1-1BFSG (AX-95918134, Chr1: 3,820,526 bp) explains 65.2-72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1BFSG (AX-273945773, Chr1: 5,623,288 bp) for grain width explains 15.5-18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% (qGT1Blink, AX-279261704, Chr1: 18,023,142 bp) and 54.9% (qGT2-1Farm, AX-154787777, Chr2: 2,118,477 bp) of the observed PVE. For the grain length-to-width ratio (LWR), the qLWR2BFSG (AX-274833045, Chr2: 10,000,097 bp) explains nearly 15.2-32% of the observed PVE. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 (qTGW6BFSG, AX-115737727, 28,484,619 bp) and explains 32.8-54% of PVE. The qTGW6BFSG QTL coincides with qGW6-1Blink for grain width and explained 32.8-54% of PVE. Putative candidate genes pooled from major QTLs for each grain trait have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.

Genetic association of spikelet abortion with spike, grain, and shoot traits in highly-diverse six-rowed barley.

Spikelet abortion is a phenomenon where apical spikelet primordia on an immature spike abort. Regardless of the row-type, both apical and basal spikelet abortion occurs, and their extent decides the number of grain-bearing spikelets retained on the spike-thus, affecting the yield potential of barley. Reducing spikelet abortion, therefore, represents an opportunity to increase barley yields. Here, we investigated the variation for apical spikelet abortion along with 16 major spike, shoot, and grain traits in a panel of 417 six-rowed spring barleys. Our analyses showed a significantly large genotypic variation resulting in high heritability estimates for all the traits. Spikelet abortion (SA) varies from 13 to 51% depending on the genotype and its geographical origin. Among the seven spike traits, SA was negatively correlated with final spikelet number, spike length and density, while positively with awn length. This positive correlation suggests a plausible role of the rapidly growing awns during the spikelet abortion process, especially after Waddington stage 5. In addition, SA also showed a moderate positive correlation with grain length, grain area and thousand-grain weight. Our hierarchical clustering revealed distinct genetic underpinning of grain traits from the spike and shoot traits. Trait associations showed a geographical bias whereby European accessions displayed higher SA and grain and shoot trait values, whereas the trend was opposite for the Asian accessions. To study the observed phenotypic variation of SA explained by 16 other individual traits, we applied linear, quadratic, and generalized additive regression models (GAM). Our analyses of SA revealed that the GAM generally performed superior in comparison to the other models. The genetic interactions among traits suggest novel breeding targets and easy-to-phenotype "proxy-traits" for high throughput on-field selection for grain yield, especially in early generations of barley breeding programs.

Patterns of genetic variation and QTLs controlling grain traits in a collection of global wheat germplasm revealed by high-quality SNP markers.

BACKGROUND: Establish a molecular breeding program involved assembling a diverse germplasm collection and generating accurate genotypes to characterize their genetic potential and associate them with agronomic traits. In this study, we acquired over eight hundred wheat accessions from international gene banks and assessed their genetic relatedness using high-quality SNP genotypes. Understanding the scope of genomic variation in this collection allows the breeders to utilize the genetic resources efficiently while improving wheat yield and quality. RESULTS: A wheat diversity panel comprising 39 durum wheat, 60 spelt wheat, and 765 bread wheat accessions was genotyped on iSelect 90 K wheat SNP arrays. A total of 57,398 SNP markers were mapped to IWGSC RefSeq v2.1 assembly, over 30,000 polymorphic SNPs in the A, B, D genomes were used to analyze population structure and diversity, the results revealed the separation of the three species and the differentiation of CIMMYT improved breeding lines and landraces or widely grown cultivars. In addition, several chromosomal regions under selection were detected. A subset of 280 bread wheat accessions was evaluated for grain traits, including grain length, width, surface area, and color. Genome-wide association studies (GWAS) revealed that several chromosomal regions were significantly linked to known quantitative trait loci (QTL) controlling grain-related traits. One of the SNP peaks at the end of chromosome 7A was in strong linkage disequilibrium (LD) with WAPO-A1, a gene that governs yield components. CONCLUSIONS: Here, the most updated and accurate physical positions of SNPs on 90 K genotyping array are provided for the first time. The diverse germplasm collection and associated genotypes are available for the wheat researchers to use in their molecular breeding program. We expect these resources to broaden the genetic basis of original breeding and pre-breeding materials and ultimately identify molecular markers associated with important agronomic traits which are evaluated in diverse environmental conditions.

Utilization of a Wheat50K SNP Microarray-Derived High-Density Genetic Map for QTL Mapping of Plant Height and Grain Traits in Wheat.

Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main quantitative trait loci (QTLs) that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map QTLs for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as recombination-rich areas in all chromosomes except chromosome 1B. Both the genetic map and the physical map showed a significant correlation, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43 cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11 mm. A total of 43 QTLs were identified, including eight QTLs for plant height (PH), 11 QTLs for thousand grain weight (TGW), 15 QTLs for grain length (GL), and nine QTLs for grain width (GW), which explained 1.36-33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci (Qph.nwafu-4B and Qph.nwafu-4D) that determined plant height. The explanation rates of phenotypic variation were 7.39-12.26% and 20.11-27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43-6.85% for phenotypic variation. Two co-segregating KASP markers were developed, and the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB, respectively. Qph.nwafu-4B, controlling plant height, and Qtgw.nwafu-4B, controlling TGW, had an obvious linkage relationship, with a distance of 7-8 cM. Breeding is based on molecular markers that control plant height and thousand-grain weight by selecting strains with low plant height and large grain weight. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43-6.85%. Three loci that affected grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2, and Qgl.nwafu-6B, illustrating the explanation rates of phenotypic variation as 6.72-9.59%, 5.62-7.75%, and 6.68-10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.

Mechanical stimulation in wheat triggers age- and dose-dependent alterations in growth, development and grain characteristics.

BACKGROUND AND AIMS: Wheat crops are exposed to a range of mechanical stimulations in their natural environment, yet we know very little about their response to such conditions. The aim of this study was to better understand the effect of mechanical stimulation on wheat growth and development, stem mechanical properties and grain measures. We focused on the following questions: (1) Does plant age affect the response to mechanical stimulation? (2) Is there a minimum threshold for the perception of mechanical stimuli? (3) Is the effect of manual brushing different to natural wind stimulation? METHODS: For age- and dose-response experiments, wheat plants were grown under controlled glasshouse conditions with brushing treatments applied using a purpose-built rig. The results of the controlled experiments are compared with those from an outside experiment where wheat plants were exposed to natural wind, with or without additional brushing. Detailed phenotypic measurements were conducted and treatment effects on grain characteristics were determined using micro-computed tomography imaging. KEY RESULTS: Two-week-old wheat plants were particularly sensitive to mechanical stimulation by controlled brushing treatments. Amongst others, plants exhibited a large reduction in height and grain yield, and an increase in tillers, above-ground biomass and stiffness of stem segments. Plants responded significantly to doses as small as one daily brushstroke. Outdoor experiments by and large confirmed results from controlled environment experiments. CONCLUSIONS: The morphological and developmental response to mechanical brushing treatment, in relation to vegetative above-ground biomass and grain yield, is dependent on plant age as well as the dose of the treatments. This study shows that mechanical stimulation of wheat impacts on a multitude of agriculturally relevant traits and provides a much needed advancement of our understanding of wheat thigmomorphogenesis and the potential applications of mechanical conditioning to control relevant traits.

µCT trait analysis reveals morphometric differences between domesticated temperate small grain cereals and their wild relatives.

Wheat and barley are two of the founder crops domesticated in the Fertile Crescent, and currently represent crops of major economic importance in temperate regions. Due to impacts on yield, quality and end-use, grain morphometric traits remain an important goal for modern breeding programmes and are believed to have been selected for by human populations. To directly and accurately assess the three-dimensional (3D) characteristics of grains, we combine X-ray microcomputed tomography (µCT) imaging techniques with bespoke image analysis tools and mathematical modelling to investigate how grain size and shape vary across wild and domesticated wheat and barley. We find that grain depth and, to a lesser extent, width are major drivers of shape change and that these traits are still relatively plastic in modern bread wheat varieties. Significant changes in grain depth are also observed to be associated with differences in ploidy. Finally, we present a model that can accurately predict the wild or domesticated status of a grain from a given taxa based on the relationship between three morphometric parameters (length, width and depth) and suggest its general applicability to both archaeological identification studies and breeding programmes.

QTL mapping for some grain traits in bread wheat (Triticum aestivum L.).

Grain traits are important agronomic attributes with the market value as well as milling yield of bread wheat. In the present study, quantitative trait loci (QTL) regulating grain traits in wheat were identified. Data for grain area size (GAS), grain width (GWid), factor form density (FFD), grain length-width ratio (GLWR), thousand grain weight (TGW), grain perimeter length (GPL) and grain length (GL) were recorded on a recombinant inbred line derived from the cross of NW1014 × HUW468 at Meerut and Varanasi locations. A linkage map of 55 simple sequence repeat markers for 8 wheat chromosomes was used for QTL analysis by Composite interval mapping. Eighteen QTLs distributed on 8 chromosomes were identified for seven grain traits. Of these, five QTLs for GLWR were found on chromosomes 1A, 6A, 2B, and 7B, three QTLs for GPL were located on chromosomes 4A, 5A and 7B and three QTLs for GAS were mapped on 5D and 7D. Two QTLs were identified on chromosomes 4A and 5A for GL and two QTLs for GWid were identified on chromosomes 7D and 6A. Similarly, two QTLs for FFD were found on chromosomes 1A and 5D. A solitary QTL for TGW was identified on chromosome 2B. For several traits, QTLs were also co-localized on chromosomes 2B, 4A, 5A, 6A, 5D, 7B and 7D. The QTLs detected in the present study may be validated for specific crosses and then used for marker-assisted selection to improve grain quality in bread wheat.

Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography.

Background: Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. Results: In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (µCT). The image analysis pipeline developed automatically identifies plant material of interest in µCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. Conclusions: Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.

Genetic Diversity and Elite Allele Mining for Grain Traits in Rice (Oryza sativa L.) by Association Mapping.

Mining elite alleles for grain size and weight is of importance for the improvement of cultivated rice and selection for market demand. In this study, association mapping for grain traits was performed on a selected sample of 628 rice cultivars using 262 SSRs. Grain traits were evaluated by grain length (GL), grain width (GW), grain thickness (GT), grain length to width ratio (GL/GW), and 1000-grain weight (TGW) in 2013 and 2014. Our result showed abundant phenotypic and genetic diversities found in the studied population. In total, 2953 alleles were detected with an average of 11.3 alleles per locus. The population was divided into seven subpopulations and the levels of linkage disequilibrium (LD) ranged from 34 to 84 cM. Genome-wide association mapping detected 10 marker trait association (MTAs) loci for GL, 1MTAs locus for GW, 7 MTAs loci for GT, 3 MTAs loci for GL/GW, and 1 MTAs locus for TGW. Twenty-nine, 2, 10, 5, and 3 elite alleles were found for the GL, GW, GT, GL/GW, and TGW, respectively. Optimal cross designs were predicted for improving the target traits. The accessions containing elite alleles for grain traits mined in this study could be used for breeding rice cultivars and cloning the candidate genes.