V. R. Pantalone

Quantitative trait loci for agronomic and seed quality traits in an F2 and F4:6 soybean population

AbstractMolecular breeding is becoming more practical as better technology emerges. The use of molecular markers in plant breeding for indirect selection of important traits can favorably impact breeding efficiency. The purpose of this research is to identify quantitative trait loci (QTL) on molecular linkage groups (MLG) which are associated with seed protein concentration, seed oil concentration, seed size, plant height, lodging, and maturity, in a population from a cross between the soybean cultivars ‘Essex’ and ‘Williams.’ DNA was extracted from F2 generation soybean leaves and amplified via polymerase chain reaction (PCR) using simple sequence repeat (SSR) markers. Markers that were polymorphic between the parents were analyzed against phenotypic trait data from the F2 and F4:6 generation. For the F2 population, significant additive QTL were Satt540 (MLG M, maturity, r2 = 0.11; height, r2 = 0.04, seed size, r2= 0.06], Satt373 (MLG L, seed size, r2 = 0.04; height, r2 = 0.14), Satt50 (MLG A1, maturity r2 = 0.07), Satt14 (MLG D2, oil, r2 = 0.05), and Satt251 (protein r2 = 0.03, oil, r2 =0.04). Significant dominant QTL for the F2 population were Satt540 (MLG M,height, r2 = 0.04; seed size, r2 = 0.06) and Satt14 (MLG D2, oil, r2 = 0.05). In the F4:6 generation significant additive QTL were Satt239 (MLGI, height, r2 = 0.02 at Knoxville, TN and r2 = 0.03 at Springfield, TN), Satt14 (MLG D2, seed size, r2 = 0.14 at Knoxville, TN), Satt373 (MLG L, protein, r2 = 0.04 at Knoxville, TN) and Satt251 (MLG B1, lodging r2 = 0.04 at Springfield, TN). Averaged over both environments in the F4:6 generation, significant additive QTL were identified as Satt251 (MLG B1, protein, r2 = 0.03), and Satt239 (MLG I, height, r2 = 0.03). The results found in this study indicate that selections based solely on these QTL would produce limited gains (based on low r2 values). Few QTL were detected to be stable across environments. Further research to identify stable QTL over environments is needed to make marker-assisted approaches more widely adopted by soybean breeders.

Genomic Regions Associated with Amino Acid Composition in Soybean

Soybean [Glycine max (L.) Merr.] is the single largest source of protein in animal feed. However, few studies have been conducted to evaluate genomic regions controlling amino acid composition in soybean. It is important to study the genetics of amino acid composition to achieve improvements through breeding. The objectives of this study were to determine the ratios between essential to non-essential (E:NE) and essential to total (E:T) amino acids, and to identify genomic regions controlling essential and non-essential amino acid composition in soybean seed. To achieve these objectives, 101 F6-derived recombinant inbred lines (RIL) developed from a cross of N87-984-16 × TN93-99 were used. Ground soybean seed samples were analyzed for amino acids using a near infrared spectroscopy (NIRS) instrument. A significant (p < 0.01) difference among the RIL was found for amino acid composition. Heritability estimates on an entry mean basis ranged from 0.13 for His to 0.67 for Tyr. A total of 94 polymorphic simple sequence repeat (SSR) molecular genetic markers were screened in DNA from progenies. Single factor ANOVA was used to identify candidate quantitative trait loci (QTL), which were then confirmed by QTL Cartographer. At least one QTL for each amino acid was detected in this population. QTL linked to molecular markers Satt143, Satt168, Satt203, Satt274 and Satt495 were associated with most of the amino acids. Phenotypic variation explained by an individual QTL ranged from 9.4 to 45.3%. QTL detected for amino acids in soybean in this experiment are expected to be useful for future breeding programs targeting development of improved soybean amino acid composition for human and animal nutrition.

Modifier QTL for fatty acid composition in soybean oil

Soybean [Glycine max (L.) Merr.] is the principal oilseed crop in the world. Soybean oil has various industrial and food applications. The quality of soybean oil is determined by its fatty acid composition. Palmitic, stearic, oleic, linoleic and linolenic are the predominant fatty acids in soybean oil. The objective of this study was to determine the associations of simple sequence repeat (SSR) molecular markers with minor differences in fatty acids in soybean oil thereby detecting modifier quantitative trait loci (QTL) which could further improve soybean oil quality. To achieve this objective, 101 F6-derived recombinant inbred lines (RIL) from a population whose parents did not contain major mutant fatty acid alleles were developed from a cross of N87-984-16 × TN93-99. Fatty acids were determined by gas chromatography. Heritability estimates on an entry mean basis for fatty acids ranged from 65.8 to 77.3% for palmitic and linoleic acids, respectively. Molecular marker Satt537 located on molecular linkage group (MLG) D1b was associated with palmitic acid and Satt168 and Satt249 located on MLG B2 and J, respectively were associated with stearic acid. Molecular markers Satt185 or Satt268 (which are within 0.6 cM of each other) located on MLG E were consistently associated with oleic and linoleic acid, and Satt263 and Satt235 located on MLG E and G, respectively were associated with linolenic acid. The lack of markers associated with multiple fatty acids suggests the possibility of independently changing fatty acid levels to achieve a desirable composition, except for regions common to all saturated fatty acids. Phenotypic variation explained by the fatty acids modifier QTL ranged from 10 to 22.5%. These modifier QTL may be useful in making minor improvements to further enhance the quality of soybean oil.

Mapping the Fas locus controlling stearic acid content in soybean

Abstract.Increasing the stearic acid content to improve soybean [Glycine max (L) Merr] oil quality is a desirable breeding objective for food-processing applications. Although a saturated fatty acid, stearic acid has been shown to reduce total levels of blood cholesterol and offers the potential for the production of solid fat products (such as margarine) without hydrogenation. This would result in the reduction of the level of trans fat in food products and alleviate some current health concerns. A segregating F2 population was developed from the cross between Dare, a normal stearic acid content cultivar, and FAM94-41, a high stearic acid content line. This population was used to assess linkage between the Fas locus and simple sequence repeat (SSR) markers. Three SSR markers, Satt070, Satt474 and Satt556, were identified to be associated with stearic acid (P < 0.0001, r2 > 0.61). A linkage map consisting of the three SSR markers and the Fas locus was then constructed in map order, Fas, Satt070, Satt474 and Satt556, with a LOD score of 3.0. Identification of these markers may be useful in molecular marker-assisted breeding programs targeting modifications in soybean fatty acids.

Construction of high resolution genetic linkage maps to improve the soybean genome sequence assembly Glyma1.01

BackgroundA landmark in soybean research, Glyma1.01, the first whole genome sequence of variety Williams 82 (Glycine max L. Merr.) was completed in 2010 and is widely used. However, because the assembly was primarily built based on the linkage maps constructed with a limited number of markers and recombinant inbred lines (RILs), the assembled sequence, especially in some genomic regions with sparse numbers of anchoring markers, needs to be improved. Molecular markers are being used by researchers in the soybean community, however, with the updating of the Glyma1.01 build based on the high-resolution linkage maps resulting from this research, the genome positions of these markers need to be mapped.ResultsTwo high density genetic linkage maps were constructed based on 21,478 single nucleotide polymorphism loci mapped in the Williams 82 x G. soja (Sieb. & Zucc.) PI479752 population with 1083 RILs and 11,922 loci mapped in the Essex x Williams 82 population with 922 RILs. There were 37 regions or single markers where marker order in the two populations was in agreement but was not consistent with the physical position in the Glyma1.01 build. In addition, 28 previously unanchored scaffolds were positioned. Map data were used to identify false joins in the Glyma1.01 assembly and the corresponding scaffolds were broken and reassembled to the new assembly, Wm82.a2.v1. Based upon the plots of the genetic on physical distance of the loci, the euchromatic and heterochromatic regions along each chromosome in the new assembly were delimited. Genomic positions of the commonly used markers contained in BARCSOYSSR_1.0 database and the SoySNP50K BeadChip were updated based upon the Wm82.a2.v1 assembly.ConclusionsThe information will facilitate the study of recombination hot spots in the soybean genome, identification of genes or quantitative trait loci controlling yield, seed quality and resistance to biotic or abiotic stresses as well as other genetic or genomic research.

The Methylome of Soybean Roots during the Compatible Interaction with the Soybean Cyst Nematode

The soybean cyst nematode induces genomewide differential DNA methylation that impacts a large number of structural genes and biological functions. The soybean cyst nematode (SCN; Heterodera glycines) induces the formation of a multinucleated feeding site, or syncytium, whose etiology includes massive gene expression changes. Nevertheless, the genetic networks underlying gene expression control in the syncytium are poorly understood. DNA methylation is a critical epigenetic mark that plays a key role in regulating gene expression. To determine the extent to which DNA methylation is altered in soybean (Glycine max) roots during the susceptible interaction with SCN, we generated whole-genome cytosine methylation maps at single-nucleotide resolution. The methylome analysis revealed that SCN induces hypomethylation to a much higher extent than hypermethylation. We identified 2,465 differentially hypermethylated regions and 4,692 hypomethylated regions in the infected roots compared with the noninfected control. In addition, 703 and 1,346 unique genes were identified as overlapping with hyper- or hypomethylated regions, respectively. The differential methylation in genes apparently occurs independently of gene size and GC content but exhibits strong preference for recently duplicated paralogs. Furthermore, a set of 278 genes was identified as specifically syncytium differentially methylated genes. Of these, we found genes associated with epigenetic regulation, phytohormone signaling, cell wall architecture, signal transduction, and ubiquitination. This study provides, to our knowledge, new evidence that differential methylation is part of the regulatory mechanisms controlling gene expression changes in the nematode-induced syncytium.

The methylome of soybean roots during the compatible interaction with the soybean cyst nematode, Heterodera glycines

The soybean cyst nematode induces genomewide differential DNA methylation that impacts a large number of structural genes and biological functions. The soybean cyst nematode (SCN; Heterodera glycines) induces the formation of a multinucleated feeding site, or syncytium, whose etiology includes massive gene expression changes. Nevertheless, the genetic networks underlying gene expression control in the syncytium are poorly understood. DNA methylation is a critical epigenetic mark that plays a key role in regulating gene expression. To determine the extent to which DNA methylation is altered in soybean (Glycine max) roots during the susceptible interaction with SCN, we generated whole-genome cytosine methylation maps at single-nucleotide resolution. The methylome analysis revealed that SCN induces hypomethylation to a much higher extent than hypermethylation. We identified 2,465 differentially hypermethylated regions and 4,692 hypomethylated regions in the infected roots compared with the noninfected control. In addition, 703 and 1,346 unique genes were identified as overlapping with hyper- or hypomethylated regions, respectively. The differential methylation in genes apparently occurs independently of gene size and GC content but exhibits strong preference for recently duplicated paralogs. Furthermore, a set of 278 genes was identified as specifically syncytium differentially methylated genes. Of these, we found genes associated with epigenetic regulation, phytohormone signaling, cell wall architecture, signal transduction, and ubiquitination. This study provides, to our knowledge, new evidence that differential methylation is part of the regulatory mechanisms controlling gene expression changes in the nematode-induced syncytium.

Molecular mapping and genomics of soybean seed protein: a review and perspective for the future

Key messageGenetic improvement of soybean protein meal is a complex process because of negative correlation with oil, yield, and temperature. This review describes the progress in mapping and genomics, identifies knowledge gaps, and highlights the need of integrated approaches.AbstractMeal protein derived from soybean [Glycine max (L) Merr.] seed is the primary source of protein in poultry and livestock feed. Protein is a key factor that determines the nutritional and economical value of soybean. Genetic improvement of soybean seed protein content is highly desirable, and major quantitative trait loci (QTL) for soybean protein have been detected and repeatedly mapped on chromosomes (Chr.) 20 (LG-I), and 15 (LG-E). However, practical breeding progress is challenging because of seed protein content’s negative genetic correlation with seed yield, other seed components such as oil and sucrose, and interaction with environmental effects such as temperature during seed development. In this review, we discuss rate-limiting factors related to soybean protein content and nutritional quality, and potential control factors regulating seed storage protein. In addition, we describe advances in next-generation sequencing technologies for precise detection of natural variants and their integration with conventional and high-throughput genotyping technologies. A syntenic analysis of QTL on Chr. 15 and 20 was performed. Finally, we discuss comprehensive approaches for integrating protein and amino acid QTL, genome-wide association studies, whole-genome resequencing, and transcriptome data to accelerate identification of genomic hot spots for allele introgression and soybean meal protein improvement.

The development and use of a molecular model for soybean maturity groups

BackgroundAchieving appropriate maturity in a target environment is essential to maximizing crop yield potential. In soybean [Glycine max (L.) Merr.], the time to maturity is largely dependent on developmental response to dark periods. Once the critical photoperiod is reached, flowering is initiated and reproductive development proceeds. Therefore, soybean adaptation has been attributed to genetic changes and natural or artificial selection to optimize plant development in specific, narrow latitudinal ranges. In North America, these regions have been classified into twelve maturity groups (MG), with lower MG being shorter season than higher MG. Growing soybean lines not adapted to a particular environment typically results in poor growth and significant yield reductions. The objective of this study was to develop a molecular model for soybean maturity based on the alleles underlying the major maturity loci: E1, E2, and E3.ResultsWe determined the allelic variation and diversity of the E maturity genes in a large collection of soybean landraces, North American ancestors, Chinese cultivars, North American cultivars or expired Plant Variety Protection lines, and private-company lines. The E gene status of accessions in the USDA Soybean Germplasm Collection with SoySNP50K Beadchip data was also predicted. We determined the E allelic combinations needed to adapt soybean to different MGs in the United States (US) and discovered a strong signal of selection for E genotypes released in North America, particularly the US and Canada.ConclusionsThe E gene maturity model proposed will enable plant breeders to more effectively transfer traits into different MGs and increase the overall efficiency of soybean breeding in the US and Canada. The powerful yet simple selection strategy for increasing soybean breeding efficiency can be used alone or to directly enhance genomic prediction/selection schemes. The results also revealed previously unrecognized aspects of artificial selection in soybean imposed by soybean breeders based on geography that highlights the need for plant breeding that is optimized for specific environments.

Phytopathogen-induced changes to plant methylomes

DNA methylation is a dynamic and reversible type of epigenetic mark that contributes to cellular physiology by affecting transcription activity, transposon mobility and genome stability. When plants are infected with pathogens, plant DNA methylation patterns can change, indicating an epigenetic interplay between plant host and pathogen. In most cases methylation can change susceptibility. While DNA hypomethylation appears to be a common phenomenon during the susceptible interaction, the levels and patterns of hypomethylation in transposable elements and genic regions may mediate distinct responses against various plant pathogens. The effect of DNA methylation on the plant immune response and other cellular activities and molecular functions is established by localized differential DNA methylation via cis-regulatory mechanisms as well as through trans-acting mechanisms. Understanding the epigenetic differences that control the phenotypic variations between susceptible and resistant interactions should facilitate the identification of new sources of resistance mediated by epigenetic mechanisms, which can be exploited to endow pathogen resistance to crops.