Biparental Populations Research Articles

Discovery and Validation of Grain Shape Loci in U.S. Rice Germplasm Through Haplotype Characterization.

Rice grain shape is a major determinant of rice market value and the end-use. We mapped quantitative trait loci (QTL) for grain shape traits in a bi-parental recombinant inbred line population (Trenasse/Jupiter) and discovered two major grain length QTLs—qGL3.1 and qGL7.1. Previously, a major grain shape gene GS3 was reported in the qGL3.1 region and grain length gene GL7 was reported to be encompassing qGL7.1 locus. The re-sequencing SNP data on the International Rice Research Institute (IRRI) 3K Rice Genome Project (RGP) panel were obtained from the IRRI SNP-Seek database for both genes and haplotype diversity was characterized for each gene in this diverse panel. United States rice germplasm was not well represented in the IRRI 3K RGP database. Therefore, a minimum SNP set was identified for each gene that could differentiate all the characterized haplotypes. These haplotypes in the 3K RGP panel were screened across 323 elite U.S. genotypes using the minimum SNP set. The screening of haplotypes and phenotype association confirmed the role of GS3 under qGL3.1. However, screening of the GL7 haplotypes in the U.S. germplasm panel showed that GL7 did not play a role in qGL7.1, and in addition, GL7.1 did not segregate in the Trenasse/Jupiter RIL population. This concluded that qGL7.1 is a novel QTL discovered on chr7 for grain shape in the Trenasse/Jupiter RIL population. A high-throughput KASP-based SNP marker for each locus (GS3 and qGL7.1) was identified and validated in elite U.S. rice germplasm to be used in an applied rice breeding program.

Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding.

The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested–untested genotypes (TG, UG) in observed–unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (−30.4%) and residual (−39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.

Rapid identification and deployment of major genes for flowering time and awn traits in common wheat.

Molecular markers are developed to accelerate deployment of genes for desirable traits segregated in a bi-parental population of recombinant inbred lines (RILs) or doubled haplotype (DH) lines for mapping. However, it would be the most effective if such markers for multiple traits could be identified in an F2 population. In this study, single nucleotide polymorphisms (SNP) chips were used to identify major genes for heading date and awn in an F2 population without developing RILs or DH lines. The population was generated from a cross between a locally adapted spring wheat cultivar “Ningmaizi119” and a winter wheat cultivar “Tabasco” with a diverse genetic background. It was found that the dominant Vrn-D1 allele could make Ningmaizi119 flowered a few months earlier than Tabasco in the greenhouse and without vernalization. The observed effects of the allele were validated in F3 populations. It was also found that the dominant Ali-A1 allele for awnless trait in Tabasco or the recessive ali-A1 allele for awn trait in Ningmaizi119 was segregated in the F2 population. The allelic variation in the ALI-A1 gene relies not only on the DNA polymorphisms in the promoter but also on gene copy number, with one copy ali-A1 in Ningmaizi119 but two copies Ali-A1 in Tabasco based on RT-PCR results. According to wheat genome sequences, cultivar “Mattis” has two copies Ali-A1 and cultivar “Spelta” has four copies Ali-A in a chromosome that was uncharacterized (ChrUN), in addition to one copy on chromosome 5A. This study rapidly characterized the effects of the dominant Vrn-D1 allele and identified the haplotype of Ali-A1 in gene copy number in the F2 segregation population of common wheat will accelerate their deployment in cycling lines in breeding.

Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification

Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1, PDIL, SAUR, and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight.

QTL Analysis for Bread Wheat Seed Size, Shape and Color Characteristics Estimated by Digital Image Processing

The size, shape, and color of wheat seeds are important traits that are associated with yield and flour quality (size, shape), nutritional value, and pre-harvest sprouting (coat color). These traits are under multigenic control, and to dissect their molecular and genetic basis, quantitative trait loci (QTL) analysis is used. We evaluated 114 recombinant inbred lines (RILs) in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative, ITMI/MP) grown in 2014 season. We used digital image analysis for seed phenotyping and obtained data for seven traits describing seed size and shape and 48 traits of seed coat color. We identified 212 additive and 34 pairs of epistatic QTLs on all the chromosomes of wheat genome except chromosomes 1A and 5D. Many QTLs were overlapping. We demonstrated that the overlap between QTL regions was low for seed size/shape traits and high for coat color traits. Using the literature and KEGG data, we identified sets of genes in Arabidopsis and rice from the networks controlling seed size and color. Further, we identified 29 and 14 candidate genes for seed size-related loci and for loci associated with seed coat color, respectively.

Phenotypic and Genetic Characterization of the Lentil Single Plant-Derived Core Collection for Resistance to Root Rot Caused by Fusarium avenaceum.

Lentil (Lens culinaris) is a pulse crop grown for its amino acid profile, moderate drought tolerance, and ability to fix nitrogen. As the global demand for lentils expands and new production regions emerge so too have the complement of diseases that reduce yield, including the root rot complex. Although the predominant causal pathogen varies based on growing region, Fusarium avenaceum is often found to be an important contributor to disease. This study screened part of the lentil single plant-derived core collection for resistance to F. avenaceum in a greenhouse. Plants were phenotyped for disease severity using three scoring scales and the differences in biomass traits due to pathogen presence were measured. Lentil accessions varied in disease severity and differences in biomass traits were found to be correlated with each visual severity estimate (r = -0.37 to -0.63, P < 0.001), however, heritability estimates were low to moderate among traits (H2 = 0.12 to 0.43). Results of a genome-wide association study (GWAS) using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing revealed 11 quantitative trait loci (QTL) across four chromosomes. Two pairs of QTL colocated for two traits and were found near putative orthologs that have been previously associated with plant disease resistance. The identification of lentil accessions that did not exhibit a difference in biomass traits may serve as parental material in breeding or in the development of biparental mapping populations to further validate and dissect the genetic control of resistance to root rot caused by F. avenaceum.

Phenotypic and Genotypic Diversity of Ascochyta fabae Populations in Southern Australia.

Ascochyta fabae Speg. is a serious foliar fungal disease of faba bean and a constraint to production worldwide. This study investigated the phenotypic and genotypic diversity of the A. fabae pathogen population in southern Australia and the pathogenic variability of the population was examined on a differential set of faba bean cultivars. The host set was inoculated with 154 A. fabae isolates collected from 2015 to 2018 and a range of disease reactions from high to low aggressiveness was observed. Eighty percent of isolates collected from 2015 to 2018 were categorized as pathogenicity group (PG) PG-2 (pathogenic on Farah) and were detected in every region in each year of collection. Four percent of isolates were non-pathogenic on Farah and designated as PG-1. A small group of isolates (16%) were pathogenic on the most resistant differential cultivars, PBA Samira or Nura, and these isolates were designated PG-3. Mating types of 311 isolates collected between 1991 and 2018 were determined and showed an equal ratio of MAT1–1 and MAT1–2 in the southern Australian population. The genetic diversity and population structure of 305 isolates were examined using DArTseq genotyping, and results suggest no association of genotype with any of the population descriptors viz.: collection year, region, host cultivar, mating type, or PG. A Genome-Wide Association Study (GWAS) was performed to assess genetic association with pathogenicity traits and a significant trait-associated genomic locus for disease in Farah AR and PBA Zahra, and PG was revealed. The high frequency of mating of A. fabae indicated by the wide distribution of the two mating types means changes to virulence genes would be quickly distributed to other genotypes. Continued monitoring of the A. fabae pathogen population through pathogenicity testing will be important to identify any increases in aggressiveness or emergence of novel PGs. GWAS and future genetic studies using biparental mating populations could be useful for identifying virulence genes responsible for the observed changes in pathogenicity.

Multi-locus genome-wide association studies reveal novel alleles for flowering time under vernalisation and extended photoperiod in a barley MAGIC population

Key messageKey genes controlling flowering and interactions of different photoperiod alleles with various environments were identified in a barley MAGIC population. A new candidate gene for vernalisation requirements was also detected.Optimal flowering time has a major impact on grain yield in crop species, including the globally important temperate cereal crop barley (Hordeum vulgare L.). Understanding the genetics of flowering is a key avenue to enhancing yield potential. Although bi-parental populations were used intensively to map genes controlling flowering, their lack of genetic diversity requires additional work to obtain desired gene combinations in the selected lines, especially when the two parental cultivars did not carry the genes. Multi-parent mapping populations, which use a combination of four or eight parental cultivars, have higher genetic and phenotypic diversity and can provide novel genetic combinations that cannot be achieved using bi-parental populations. This study uses a Multi-parent advanced generation intercross (MAGIC) population from four commercial barley cultivars to identify genes controlling flowering time in different environmental conditions. Genome-wide association studies (GWAS) were performed using 5,112 high-quality markers from Diversity Arrays Technology sequencing (DArT-seq), and Kompetitive allele-specific polymerase chain reaction (KASP) genetic markers were developed. Phenotypic data were collected from fifteen different field trials for three consecutive years. Planting was conducted at various sowing times, and plants were grown with/without additional vernalisation and extended photoperiod treatments. This study detected fourteen stable regions associated with flowering time across multiple environments. GWAS combined with pangenome data highlighted the role of CEN gene in flowering and enabled the prediction of different CEN alleles from parental lines. As the founder lines of the multi-parental population are elite germplasm, the favourable alleles identified in this study are directly relevant to breeding, increasing the efficiency of subsequent breeding strategies and offering better grain yield and adaptation to growing conditions.

A joint learning approach for genomic prediction in polyploid grasses

Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.

Artificial Neural Network for Discrimination and Classification of Tropical Soybean Genotypes of Different Relative Maturity Groups

Soybean has a recognized narrow genetic base that often makes it difficult to visualize available genetic and phenotypic variability and identify superior genotypes during the selection process. However, the phenotypic expression of soybean plants is highly affected by photoperiod and the cultivation of a given variety is performed in the latitude range that presents ideal conditions for its development based on its relative maturity group (RMG) for the optimization of the phenotypic expression of its genotype. Based on the above, this study aimed to evaluate the efficiency of artificial neural networks (ANNs) as a tool for the correct discrimination and classification of tropical soybean genotypes according to their relative maturity group during the population selection process with the aim of optimizing the phenotypic performance of these selected genotypes. For this purpose, three biparental populations were synthesized, one with a wide genetic variability for the RMG character obtained from the hybridization between genitors of maturity groups RMG 5 (Sub-tropical 23° LS) × RMG 9.4 (Tropical 0° LS) and two populations with a narrow variability obtained between genitors RMG 7.3 (Tropical 20° LS) × RMG 9.4 and RMG 5.3 × RMG 6.7, respectively. Criteria for comparing the developed ANN architecture with Fisher’s linear and Anderson’s quadratic parametric discriminant methodologies were applied to the data for the discrimination and classification of the genotypes. ANN showed an apparent error rate of less than 8.16% as well as a low influence of environmental factors, correctly classifying the genotypes in the populations even in cases of reduced genetic variability such as in the RMG 5 × RMG 6 population. In contrast, the discriminant functions were inefficient in correctly classifying the genotypes in the populations with genealogical similarity (RMG 5 × RMG 6) and wide genetic variability, with an error rate of more than 50%. Based on the results of this study, ANN can be used for the discrimination of genotypes in the initial generations of selection in breeding programs for the development of high performance cultivars for wide and reduced photoperiod amplitudes, even with fewer selection environments, more efficiently, and with fewer time and resources applied. As a result of similarity between the parents, ANN can correctly classify genotypes from populations with a narrow genetic base, in addition to pure lines and genotypes with a high degree of inbreeding.

Quantitative Trait Locus Mapping of Marsh Spot Disease Resistance in Cranberry Common Bean (Phaseolus vulgaris L.).

Common bean (Phaseolus vulgaris L.) is a food crop that is an important source of dietary proteins and carbohydrates. Marsh spot is a physiological disorder that diminishes seed quality in beans. Prior research suggested that this disease is likely caused by manganese (Mn) deficiency during seed development and that marsh spot resistance is controlled by at least four genes. In this study, genetic mapping was performed to identify quantitative trait loci (QTL) and the potential candidate genes associated with marsh spot resistance. All 138 recombinant inbred lines (RILs) from a bi-parental population were evaluated for marsh spot resistance during five years from 2015 to 2019 in sandy and heavy clay soils in Morden, Manitoba, Canada. The RILs were sequenced using a genotyping by sequencing approach. A total of 52,676 single nucleotide polymorphisms (SNPs) were identified and filtered to generate a high-quality set of 2066 SNPs for QTL mapping. A genetic map based on 1273 SNP markers distributed on 11 chromosomes and covering 1599 cm was constructed. A total of 12 stable and 4 environment-specific QTL were identified using additive effect models, and an additional two epistatic QTL interacting with two of the 16 QTL were identified using an epistasis model. Genome-wide scans of the candidate genes identified 13 metal transport-related candidate genes co-locating within six QTL regions. In particular, two QTL (QTL.3.1 and QTL.3.2) with the highest R2 values (21.8% and 24.5%, respectively) harbored several metal transport genes Phvul.003G086300, Phvul.003G092500, Phvul.003G104900, Phvul.003G099700, and Phvul.003G108900 in a large genomic region of 16.8–27.5 Mb on chromosome 3. These results advance the current understanding of the genetic mechanisms of marsh spot resistance in cranberry common bean and provide new genomic resources for use in genomics-assisted breeding and for candidate gene isolation and functional characterization.

Screening Technique Based on Seed and Early Seedling Parameters for Cold Tolerance of Selected F2-Derived F3 Rice Genotypes under Controlled Conditions

The cold tolerance studies were carried out in a bi-parental F2 population of a cross between tolerant and susceptible parents (SKUA-529 and HEERA, respectively). The purpose was to screen the individuals of a population for primary cold-tolerance-related attributes. The information generated has a direct application and use in identifying cold tolerance quantitative trait loci (QTLs) and further can be used for genotyping with an appropriate marker system. The screening was carried out on F2-derived F3 seeds and F3 plants for seedling and agronomic traits, respectively. Two tests measuring cold tolerance were conducted. In experiment I, seeds were germinated for 28 days at 13 °C and 7 days at 28 °C, and in experiment II, the seeds were germinated for 72 h at 28 °C, 96 h at 13 °C, and once more for 72 h at 28 °C. Coleoptile length, germination percentage, and radical reduction percentages were all measured in experiment I. The radicle and coleoptile regeneration in experiment II were measured after the cold period. The improvement in cold tolerance was achieved through radicle regrowth, as evidenced by the difference between the second and first measurements. The individual lines from the F2:3 population that recorded high germination (%) were #21, #13, #14, and #15. The percentage of coleoptile length (PERCOL %) was observed to be between the ranges of 23.33% to 53.00%. The reduction in coleoptile length (REDCOL %) was also obtained, and there was less reduction in #15, #16, and #14 and it had a range between 38.46% and 75%. Radicle regrowth (REDRAG) was high at 13 °C in #7, #11, #30, #35, and #36. Survival of the seedling range was between 33.33% and up to 100%, and the highest survival rate was observed in #16. The main objective of this rotation in temperature was to emulate field conditions where there has been a drop in temperature. The evaluations were done for primary cold stress tolerance traits, and it was found that most of these traits exhibited high variability. The mapping population developed may be utilized to generate a linkage map and locate QTLs for tolerance to cold stress in rice. Further, the identified donors for cold tolerance may be utilized for breeding programs aimed at the transfer of low-temperature stress tolerance into susceptible backgrounds. In general, a genotype with improved seedling germination rates, growth rates, and leaf yellowing scores; high seedling survival; lesser reduction in coleoptile length and in radicle development; and recovery following a cold shock at the seedling stage demonstrated its cold resistance. Genotypes with a low germination percentage, a greater number of days to germination, slow growth rate and higher leaf yellowing score, high reduction in coleoptile and radicle growth, and reduced seedling survival indicated cold susceptibility.

Development and Validation of Diagnostic KASP Markers for Brown Planthopper Resistance in Rice.

Rice (Oryza sativa L.) is an important source of nutrition for the world’s burgeoning population that often faces yield loss due to infestation by the brown planthopper (BPH, Nilaparvata lugens (Stål)). The development of rice cultivars with BPH resistance is one of the crucial precedences in rice breeding programs. Recent progress in high-throughput SNP-based genotyping technology has made it possible to develop markers linked to the BPH more quickly than ever before. With this view, a genome-wide association study was undertaken for deriving marker-trait associations with BPH damage scores and SNPs from genotyping-by-sequencing data of 391 multi-parent advanced generation inter-cross (MAGIC) lines. A total of 23 significant SNPs involved in stress resistance pathways were selected from a general linear model along with 31 SNPs reported from a FarmCPU model in previous studies. Of these 54 SNPs, 20 were selected in such a way to cover 13 stress-related genes. Kompetitive allele-specific PCR (KASP) assays were designed for the 20 selected SNPs and were subsequently used in validating the genotypes that were identified, six SNPs, viz, snpOS00912, snpOS00915, snpOS00922, snpOS00923, snpOS00927, and snpOS00929 as efficient in distinguishing the genotypes into BPH-resistant and susceptible clusters. Bph17 and Bph32 genes that are highly effective against the biotype 4 of the BPH have been validated by gene specific SNPs with favorable alleles in M201, M272, M344, RathuHeenati, and RathuHeenati accession. These identified genotypes could be useful as donors for transferring BPH resistance into popular varieties with marker-assisted selection using these diagnostic SNPs. The resistant lines and the significant SNPs unearthed from our study can be useful in developing BPH-resistant varieties after validating them in biparental populations with the potential usefulness of SNPs as causal markers.

Rose Rosette Disease Resistance Loci Detected in Two Interconnected Tetraploid Garden Rose Populations.

Rose rosette disease (RRD), caused by the Rose rosette emaravirus (RRV), is a major threat to the garden rose industry in the United States. There has been limited work on the genetics of host plant resistance to RRV. Two interconnected tetraploid garden rose F1 biparental mapping populations were created to develop high-quality tetraploid rose linkage maps that allowed the discovery of RRD resistance quantitative trait loci (QTLs) on linkage groups (LGs) 5, 6, and 7. These QTLs individually accounted for around 18–40% of the phenotypic variance. The locus with the greatest effect on partial resistance was found in LG 5. Most individuals with the LG 5 QTL were in the simplex configuration; however, two individuals were duplex (likely due to double reduction). Identification of resistant individuals and regions of interest can help the development of diagnostic markers for marker-assisted selection in a breeding program.

Differential responses of soybean genotypes to off‐target dicamba damage

AbstractSince the commercialization and widespread adoption of dicamba‐tolerant (DT) soybean cultivars across the United States, numerous cases of off‐target damage to non‐DT soybean have been reported. Soybean is naturally highly sensitive to dicamba, a synthetic auxin herbicide. Previous studies have focused on understanding the impact of growth stage, dosage, frequency, and duration of dicamba exposure on the severity of symptomology and yield loss. To date, little research has investigated the effect of genetic components in the observed responses. Therefore, this study was conducted to estimate yield losses caused by prolonged off‐target dicamba exposure and to identify genotypes with varying responses to off‐target damage. A total of 553 soybean genotypes derived from 239 unique biparental populations were evaluated in nine environments over 3 yr. A yield penalty of 8.8% was observed for every increment in damage score on a 1–4 scale with losses as high as 40%. Although the interaction between damage and maturity group (MG) significantly affected yield, genotypes showing the most tolerance had similar yields independent of their MG. This indicated that natural tolerance to off‐target dicamba may be conferred by physiological mechanisms other than the length of the recovery window. Given the widespread adoption of DT systems and potential yield losses in non‐DT soybean genotypes, identification of non‐DT soybean genotypes with higher tolerance to off‐target dicamba may sustain and improve the production of other non‐DT herbicide soybean production systems, including the niche markets of organic and conventional soybean.

Identification and Validation of Quantitative Trait Loci for Grain Size in Bread Wheat (Triticum aestivum L.)

Grain width (GW) and grain length (GL) are crucial components affecting grain weight. Dissection of their genetic control is essential for improving yield potential in wheat breeding. Yangmai 12 (YM12) and Yanzhan 1 (YZ1) are two elite cultivars released in the Middle and Lower Yangtze Valleys Wheat Zone (MLYVWZ) and the Yellow-Huai River Valleys Wheat Zone (YRVWZ), respectively. One biparental population derived from YM12/YZ1 cross was employed to perform QTL mapping based on the data from four environments over two years to detect quantitative trait loci (QTL) for GW and GL. A total of eight QTL were identified on chromosomes 1B, 2D, 3B, 4B, 5A, and 6B. Notably, QGW.yz.2D was co-located with QGL.yz.2D, and QGW.yz.4B was co-located with QGL.yz.4B, respectively. QGW.yz.2D and QGL.yz.2D, with the increasing GW/GL allele from YZ1, explained 12.36–18.27% and 13.69–26.53% of the phenotypic variations for GW and GL, respectively. QGW.yz.4B and QGL.yz.4B, with the increasing GW/GL allele from YM12, explained 10.34–11.95% and 10.35–16.04% of the phenotypic variation for GW and GL, respectively. QGL.yz.5A, with the increasing GL allele from YM12, explained 10.04–12.48% of the phenotypic variation for GL. Moreover, the positive alleles of these three QTL regions could significantly increase thousand-grain weight, and QGW.yz.4B/QGL.yz.4B and QGL.yz.5A did not show significant negative effects on grain number per spike. QGL.yz.2D, QGW.yz.4B/QGL.yz.4B, and QGL.yz.5A have not been reported. These three QTL regions were then further validated using Kompetitive Allele-Specific PCR (KASP) markers in 159 wheat cultivars/lines from MLYVWZ and YRVWZ. Combining the positive alleles of the major QTL significantly increased GW and GL. Eleven candidate genes associated with encoding ethylene-responsive transcription factor, oleosin, osmotin protein, and thaumatin protein were identified. Three major QTL and KASP markers reported here will be helpful in developing new wheat cultivars with high and stable yields.

Identification of QTLs Associated With Agronomic Traits in Tobacco via a Biparental Population and an Eight-Way MAGIC Population.

Agronomic traits such as plant height (PH), leaf number (LN), leaf length (LL), and leaf width (LW), which are closely related to yield and quality, are important in tobacco (Nicotiana tabacum L.). To identify quantitative trait loci (QTLs) associated with agronomic traits in tobacco, 209 recombinant inbred lines (RILs) and 537 multiparent advanced generation intercross (MAGIC) lines were developed. The biparental RIL and MAGIC lines were genotyped using a 430 K single-nucleotide polymorphism (SNP) chip assay, and their agronomic traits were repeatedly evaluated under different conditions. A total of 43 QTLs associated with agronomic traits were identified through a combination of linkage mapping (LM) and association mapping (AM) methods. Among these 43 QTLs, three major QTLs, namely qPH13-3, qPH17-1, and qLW20-1, were repeatedly identified by the use of various genetically diverse populations across different environments. The candidate genes for these major QTLs were subsequently predicted. Validation and utilization of the major QTL qLW20-1 for the improvement of LW in tobacco were investigated. These results could be applied to molecular marker-assisted selection (MAS) for breeding important agronomic traits in tobacco.

The construction of a high-density consensus genetic map for soybean based on SNP markers derived from genotyping-by-sequencing.

Genetic linkage maps are used to localize markers on the genome based on the recombination frequency. Most often, these maps are based on the segregation observed within a single biparental population of limited size (n<300) where relatively few recombination events are sampled and in which some genomic regions are monomorphic because both parents carry the same alleles. Together, these two limitations affect both the resolution and extent of genome coverage of such maps. Consensus genetic maps overcome the limitations of individual genetic maps by merging the information from multiple segregating populations derived from a greater diversity of parental combinations, thus increasing the number of recombination events and reducing the number of monomorphic regions. The aim of this study was to construct a high-density consensus genetic map for single nucleotide polymorphism (SNP) markers obtained through a genotyping-by-sequencing (GBS) approach. Individual genetic maps were generated from six F4:5 mapping populations (n=278-365), totaling 1857 individuals. The six linkage maps were then merged to produce a consensus map comprising a total of 16 311 mapped SNPs that jointly cover 99.5% of the soybean genome with only two gaps larger than 10 cM. Compared to previous soybean consensus maps, it offers a more extensive and uniform coverage.

Genetic control of rhizosheath formation in pearl millet

The rhizosheath, the layer of soil that adheres strongly to roots, influences water and nutrients acquisition. Pearl millet is a cereal crop that plays a major role for food security in arid regions of sub-Saharan Africa and India. We previously showed that root-adhering soil mass is a heritable trait in pearl millet and that it correlates with changes in rhizosphere microbiota structure and functions. Here, we studied the correlation between root-adhering soil mass and root hair development, root architecture, and symbiosis with arbuscular mycorrhizal fungi and we analysed the genetic control of this trait using genome wide association (GWAS) combined with bulk segregant analysis and gene expression studies. Root-adhering soil mass was weakly correlated only to root hairs traits in pearl millet. Twelve QTLs for rhizosheath formation were identified by GWAS. Bulk segregant analysis on a biparental population validated five of these QTLs. Combining genetics with a comparison of global gene expression in the root tip of contrasted inbred lines revealed candidate genes that might control rhizosheath formation in pearl millet. Our study indicates that rhizosheath formation is under complex genetic control in pearl millet and suggests that it is mainly regulated by root exudation.

SEA v2.0: an R software package for mixed major genes plus polygenes inheritance analysis of quantitative traits

<p id="C3">The phenotypic values for quantitative trait from bi-parental segregation populations can be used to identify its mixed major genes plus polygenes inheritance model, which provides important information for the genetic basis of quantitative traits and crop breeding. To comprehensively summarize the research results of methodological advances, add the new functions of the software and correct its shortcomings in previous versions, an R software package SEA v2.0 with interactive graphical user interface is developed under R studio-1.4.1103 platform and R environment. In this software, there were 14 types of bi-parental segregation populations, and each type included four modules: data input, data analysis, posterior probability calculation, and distribution curve drawing. To save running time, doParallel was used to conduct parallel computing, data.table was used to quickly read and write datasets, and MASS was used to estimate the parameters in component distributions. KScorrect, kolmim, and shiny packages were used to simplify the programs. As long as users uploaded the data file with *.csv format and set the related parameters, the results could be quickly displayed. The software was validated by real data analysis of soybean podding habit and Monte Carlo simulation studies, and can be downloaded from https://cran.r-project.org/web/packages/SEA/index.html.