Guangnan Xing

Exploration of presence/absence variation and corresponding polymorphic markers in soybean genome.

This study was designed to reveal the genome-wide distribution of presence/absence variation (PAV) and to establish a database of polymorphic PAV markers in soybean. The 33 soybean whole-genome sequences were compared to each other with that of Williams 82 as a reference genome. A total of 33,127 PAVs were detected and 28,912 PAV markers with their primer sequences were designed as the database NJAUSoyPAV_1.0. The PAVs scattered on whole genome while only 518 (1.8%) overlapped with simple sequence repeats (SSRs) in BARCSOYSSR_1.0 database. In a random sample of 800 PAVs, 713 (89.13%) showed polymorphism among the 12 differential genotypes. Using 126 PAVs and 108 SSRs to test a Chinese soybean germplasm collection composed of 828 Glycine soja Sieb. et Zucc. and Glycine max (L.) Merr. accessions, the per locus allele number and its variation appeared less in PAVs than in SSRs. The distinctness among alleles/bands of PCR (polymerase chain reaction) products showed better in PAVs than in SSRs, potential in accurate marker-assisted allele selection. The association mapping results showed SSR + PAV was more powerful than any single marker systems. The NJAUSoyPAV_1.0 database has enriched the source of PCR markers, and may fit the materials with a range of per locus allele numbers, if jointly used with SSR markers.

Establishment of a 100-seed weight quantitative trait locus–allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes

A representative sample comprising 366 accessions from the Chinese soybean landrace population (CSLRP) was tested under four growth environments for determination of the whole-genome quantitative trait loci (QTLs) system of the 100-seed weight trait (ranging from 4.59g to 40.35g) through genome-wide association study (GWAS). A total of 116 769 single nucleotide polymorphisms (SNPs) were identified and organized into 29 121 SNP linkage disequilibrium blocks (SNPLDBs) to fit the property of multiple alleles/haplotypes per locus in germplasm. An innovative two-stage GWAS was conducted using a single locus model for shrinking the marker number followed by a multiple loci model utilizing a stepwise regression for the whole-genome QTL identification. In total, 98.45% of the phenotypic variance (PV) was accounted for by four large-contribution major QTLs (36.33%), 51 small-contribution major QTLs (43.24%), and a number of unmapped minor QTLs (18.88%), with the QTL×environment variance representing only 1.01% of the PV. The allele numbers of each QTL ranged from two to 10. A total of 263 alleles along with the respective allele effects were estimated and organized into a 263×366 matrix, giving the compact genetic constitution of the CSLRP. Differentiations among the ecoregion matrices were found. No landrace had alleles which were all positive or all negative, indicating a hidden potential for recombination. The optimal crosses within and among ecoregions were predicted, and showed great transgressive potential. From the QTL system, 39 candidate genes were annotated, of which 26 were involved with the gene ontology categories of biological process, cellular component, and molecular function, indicating that diverse genes are involved in directing the 100-seed weight.

Genome-wide genetic dissection of germplasm resources and implications for breeding by design in soybean

“Breeding by Design” as a concept described by Peleman and van der Voort aims to bring together superior alleles for all genes of agronomic importance from potential genetic resources. This might be achievable through high-resolution allele detection based on precise QTL (quantitative trait locus/loci) mapping of potential parental resources. The present paper reviews the works at the Chinese National Center for Soybean Improvement (NCSI) on exploration of QTL and their superior alleles of agronomic traits for genetic dissection of germplasm resources in soybeans towards practicing “Breeding by Design”. Among the major germplasm resources, i.e. released commercial cultivar (RC), farmers’ landrace (LR) and annual wild soybean accession (WS), the RC was recognized as the primary potential adapted parental sources, with a great number of new alleles (45.9%) having emerged and accumulated during the 90 years’ scientific breeding processes. A mapping strategy, i.e. a full model procedure (including additive (A), epistasis (AA), A × environment (E) and AA × E effects), scanning with QTLNetwork2.0 and followed by verification with other procedures, was suggested and used for the experimental data when the underlying genetic model was usually unknown. In total, 110 data sets of 81 agronomically important traits were analyzed for their QTL, with 14.5% of the data sets showing major QTL (contribution rate more than 10.0% for each QTL), 55.5% showing a few major QTL but more small QTL, and 30.0% having only small QTL. In addition to the detected QTL, the collective unmapped minor QTL sometimes accounted for more than 50% of the genetic variation in a number of traits. Integrated with linkage mapping, association mappings were conducted on germplasm populations and validated to be able to provide complete information on multiple QTL and their multiple alleles. Accordingly, the QTL and their alleles of agronomic traits for large samples of RC, LR and WS were identified and then the QTL-allele matrices were established. Based on which the parental materials can be chosen for complementary recombination among loci and alleles to make the crossing plans genetically optimized. This approach has provided a way towards breeding by design, but the accuracy will depend on the precision of the loci and allele matrices.

Association Analysis between Agronomic-Processing Traits and SSR Markers and Genetic Dissection of Specific Accessions in Chinese Wild Soybean Population

Association analysis is potential in genetic dissection of germplasm accessions and breeding materials for designing crosses and improving selection efficiencies. The present study was aimed at finding elite QTLs/alleles as well as their carriers through genetic dissection of agronomic-processing traits in Chinese annual wild soybean (Glycine soja Sieb. et Zucc.) population for improvement and broadening the genetic background of modern soybean cultivars. The genotypic data of 204 simple-sequence repeat (SSR) markers on 174 wild accessions sampled from and evenly distributed in all the wild soybean eco-regions in China were used and analyzed for association with six agronomic and processing traits under TASSEL GLM (general linear model) program based on the population structure analysis. The QTLs significantly associated with the traits were analyzed further for their allele effects. The results showed: (1) Fifty-one SSR loci (times) associated with the six agronomic-processing traits were identified in the wild population. There were a few markers/loci associated with two or more traits simultaneously, which might be the genetic bases of correlation among the traits. Sixteen of fifty-one associated loci (times) were in agreement with mapped QTLs from linkage mapping procedure. (2)There existed only a few association loci in wild population coincided with those in landrace and released cultivar populations, indicating the difference of genetic structure among the three kinds of populations. (3) A set of elite alleles of detected loci and their carrier materials were screened out. Alleles for loci associated with several traits had different phenotypic effects in different traits, e.g. GMES5532a-A332 had positive phenotypic effect for both 100-seed weight and seedling death rate under submergence, while GMES5532a-A344 had negative effect on 100-seed weight but positive effect on seedling death rate under submergence. (4)There showed great difference of the genetic structure among the tested materials with extreme phenotypic value. The extreme accessions possessed the alleles with bigger effects, such as N23349 containing four alleles with bigger positive effects having its 100-seed weight as high as 9.08 g, while N23387 containing four alleles with bigger negative effects having its 100-seed weight only 0.75 g. The above results implied that association mapping could offer further genetic information complementary to linkage mapping, especially the information of multiple alleles of QTL on whole genome could be used in cross design for pyramiding elite alleles and marker-assisted selection in breeding for soybean.

Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection

Experimental error control is very important in quantitative trait locus (QTL) mapping. Although numerous statistical methods have been developed for QTL mapping, a QTL detection model based on an appropriate experimental design that emphasizes error control has not been developed. Lattice design is very suitable for experiments with large sample sizes, which is usually required for accurate mapping of quantitative traits. However, the lack of a QTL mapping method based on lattice design dictates that the arithmetic mean or adjusted mean of each line of observations in the lattice design had to be used as a response variable, resulting in low QTL detection power. As an improvement, we developed a QTL mapping method termed composite interval mapping based on lattice design (CIMLD). In the lattice design, experimental errors are decomposed into random errors and block-within-replication errors. Four levels of block-within-replication errors were simulated to show the power of QTL detection under different error controls. The simulation results showed that the arithmetic mean method, which is equivalent to a method under random complete block design (RCBD), was very sensitive to the size of the block variance and with the increase of block variance, the power of QTL detection decreased from 51.3% to 9.4%. In contrast to the RCBD method, the power of CIMLD and the adjusted mean method did not change for different block variances. The CIMLD method showed 1.2- to 7.6-fold higher power of QTL detection than the arithmetic or adjusted mean methods. Our proposed method was applied to real soybean (Glycine max) data as an example and 10 QTLs for biomass were identified that explained 65.87% of the phenotypic variation, while only three and two QTLs were identified by arithmetic and adjusted mean methods, respectively.

Identification of QTL/segments related to seed-quality traits in G. soja using chromosome segment substitution lines

Annual wild soybean characterized by low 100-seed weight (100SW), high protein content (PRC) and low oil content (OIC) may have favourable exotic genes/alleles for broadening the genetic base of the cultivated soybean. To evaluate the wild alleles/segments, a chromosome segment substitution line population comprising 151 lines with N24852 (wild) as the donor and NN1138-2 (cultivated) as the recurrent parent was analysed using single-marker analysis, interval mapping, inclusive composite interval mapping and mixed linear composite interval mapping. On 14 segments of ten chromosomes, 17 quantitative trait loci (QTL) were identified, with two segments each containing two QTL for 100SW and OIC and one segment containing two QTL for PRC and OIC, respectively. All the seven wild alleles/segments for 100SW were associated with negative effects and three were associated with positive effects, but one was associated with a negative effect for PRC, and five were associated with negative effects, but one was associated with a positive effect for OIC. Except Satt216 and Sat_224 for 100SW, the identified QTL/segments have been reported from cultivated soybean mapping populations. The detected wild segments may provide materials for further characterization, cloning and pyramiding of the alleles conferring the seed-quality traits.

Identification of major responding proteins of abnormal leaf and flower in soybean with an integrative omics strategy

Proteomics has been utilized as an effective approach to bridge the gap between phenotype and genome sequence, however, more effective strategies need to be explored to find target gene(s) from many differentially expressed proteins (DEPs). Here, we utilized an interdisciplinary approach employing a range of methodologies and software tools of genomics, proteomics and metabolomics to identify important responding proteins of the Abnormal Leaf and Flower (ALF) gene involved in soybean leaf and flower development, then to get a global insight into the relevant regulating networks underpinning the alf phenotype. The main results were as follows: (1) A pair of soybean near-isogenic lines (NILs), i.e. NJS-10H-W and NJS-10H-M, differing from ALF locus, was developed with highly consistent genetic background verified by 167 simple sequence repeats (SSR) molecular markers, and an optimized 2-DE procedure was established to separate the whole proteins of leaves of the NILs. Among more than 1000 visualized protein spots, 58 spots presented expression difference, of which 41 proteins were successfully identified by mass spectrometry. The DEPs distributed on all the twenty soybean chromosomes, indicating a complicated regulation network involved in the development of leaf and flower in soybean. (2) The ALF gene was located at the end of the short arm of linkage group C1 (Chromosome 4) by gene mapping method using an F2 population. Three DEPs were also detected in the same region. (3) Ten proteins/genes of DEPs were located in the metabolism pathway by Kyoto Encyclopedia of Genes and Genomes Application Programming Interface (KEGG API), and most of the defects occurred at intersections among carbohydrate, amino acid, energy and cofactors and vitamins metabolism. The Gene Ontology (GO) annotation results of DEPs demonstrated considerable part of proteins as DNA-binding factors, metalloproteases and oxidoreduction enzymes. The GSA (Glutamate-1-Semialdehyde2,1-Aminomutase) and PIN (Peptidyl-prolyl cis-trans isomerase) genes were selected as potential candidate genes for ALF locus based on the affluent information from different “omics” analyses, and the possible regulating profile underpinning the phenome of the mutant was also inferred. In conclusion, some important responding proteins as upstream regulated factors within ALF expression network were identified and marked to the involved pathways for further analysis of the target gene. It showed that combination of “omics” methods could accelerate the process to isolate new gene(s) and provide potential information for further study on genes and proteins regulatory network.

Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure

Key messageEighty-six R1 QTLs accounting for 89.92% phenotypic variance in a soybean RIL population were identified using RTM-GWAS with SNPLDB marker which performed superior over CIM and MLM-GWAS with BIN/SNPLDB marker.AbstractA population (NJRIKY) composed of 427 recombinant inbred lines (RILs) derived from Kefeng-1 × NN1138-2 (MGII × MGV, MG maturity group) was applied for detecting flowering date (R1) quantitative trait locus (QTL) system in soybean. From a low-depth re-sequencing (~ 0.75 ×), 576,874 SNPs were detected and organized into 4737 BINs (recombination breakpoint determinations) and 3683 SNP linkage disequilibrium blocks (SNPLDBs), respectively. Using the association mapping procedures “Restricted Two-stage Multi-locus Genome-wide Association Study” (RTM-GWAS), “Mixed Linear Model Genome-wide Association Study” (MLM-GWAS) and the linkage mapping procedure “Composite Interval Mapping” (CIM), 67, 36 and 10 BIN-QTLs and 86, 14 and 23 SNPLDB-QTLs were detected with their phenotypic variance explained (PVE) 88.70–89.92% (within heritability 98.2%), 146.41–353.62% (overflowing) and 88.29–172.34% (overflowing), respectively. The RTM-GWAS with SNPLDBs which showed to be more efficient and reasonable than the others was used to identify the R1 QTL system in NJRIKY. The detected 86 SNPLDB-QTLs with their PVE from 0.02 to 30.66% in a total of 89.92% covered 51 out of 104 R1 QTLs in 18 crosses in SoyBase and 26 out of 139 QTLs in a nested association mapping population, while the rest 29 QTLs were novel ones. From the QTL system, 52 candidate genes were annotated, including the verified gene E1, E2, E9 and J, and grouped into 3 categories of biological processes, among which 24 genes were enriched into three protein–protein interaction networks, suggesting gene networks working together. Since NJRIKY involves only MGII and MGV, the QTL/gene system among MG000–MGX should be explored further.

Identifying QTL–allele system of seed protein content in Chinese soybean landraces for population differentiation studies and optimal cross predictions

Soybean originated in ancient China has been quickly extended globally as a major protein and oil crop. The QTL–allele constitution of seed protein content (SPC) in the Chinese soybean landrace population (CSLRP) was studied using a representative sample composed of 365 accessions tested under multiple environments and analysed under the novel restricted two-stage multi-locus genome-wide association study (RTM-GWAS) procedure based on 29,121 SNPLDB (single nucleotide polymorphism linkage disequilibrium blocks) markers. The SPC varied from 37.51 to 50.46% among accessions, for which 89 QTLs, each with 2–9 alleles in a total of 255 alleles were identified, accounting for 83.16% of the phenotypic variation covering most of the genetic variation (h2 = 84.31%). The QTL–alleles of the 365 landraces were organized into a 255 × 365 QTL–allele matrix as the compact form of SPC genetic constitution in CSLRP. Of the 89 QTLs, 53 showed significantly differentiated allele frequency distribution patterns among geographic eco-regions (sub-populations). There were 32.09% alleles not common among sub-populations but found only in some sub-populations; new allele(s) emerged on some loci in some respective sub-populations, with Eco-region III showing less but Eco-region VI more emergence. The QTL–allele matrix was also used for prediction of optimal crosses for breeding purpose to reach a 99th percentile potential of up to 54.81%, more than the highest accession (50.46%). From the 89 QTLs, 59 SPC candidate genes involving biological processes, cellular components and molecular functions were annotated. Among them, Glyma18g13574 and Glyma20g21370 were inferred as two of the major SPC genes in the whole genome.

QTL Mapping of Pubescence Density and Length on Leaf Surface of Soybean

Soybean pubescences are known to play important roles in resistance to pests and tolerance to drought stress. QTL mapping of leaf pubescence density and length was conducted in recombinant inbred line populations of NJRIKY (KY) and NJRIXG (XG). The results obtained were as follows: (1) There existed great variation and certain transgressive segregation in leaf pubescence density and length among lines; highly significant negative correlations (r = 0.49 and 0.62, respectively) between the two traits were observed; the heritability values for pubescence density ranged from 75.7% to 76.8%, higher than that for pubescence length ranged from 45.2% to 62.9% in the two populations. (2) Two major QTL for pubescence density detected were PD1-1 accounted for 20.7% of phenotypic variation in XG, and PD12-1 contributed 21.7% of phenotypic variation in KY. The genetic constitution of pubescence density was composed of additive QTL (20.7 36.2% of phenotypic variation), epistatic QTL pairs (0 1.4%) and collective unmapped minor QTL (38.1 56.1%) in the two populations. Here the unmapped minor QTL ac- counted for the most part for the trait, which was not recognized if only using mapping procedures without the consideration of the total genetic variation among the lines. (3) The phenotypic variation of pubescence length in KY was accounted for by epistatic QTL pairs (4.2%) and collective unmapped minor QTL (58.7%) without additive QTL (0%), while that in XG mainly byadditive QTL, including PL1-1 and PL12-1 on chromosomes 1 and 12 accounting for 18.3% and 22.5% of phenotypic variation, respectively, with very small contribution by epistatic QTL pair and collective unmapped minor QTL. Therefore, the genetic constitutions of pubescence length in the two populations were different from each other. The genetic mechanisms of leaf pubescence density and length in soybean are complicated and involve many genes/QTL with different effects.