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

Efficient identification of QTL for agronomic traits in foxtail millet (Setaria italica) using RTM- and MLM-GWAS

KEY MESSAGE

Eleven QTLs for agronomic traits were identified by RTM- and MLM-GWAS, putative candidate genes were predicted and two markers for grain weight were developed and validated. Foxtail millet (Setaria italica), the second most cultivated millet crop after pearl millet, is an important grain crop in arid regions. Seven agronomic traits of 408 diverse foxtail millet accessions from 15 provinces in China were evaluated in three environments. They were clustered into two divergent groups based on genotypic data using ADMIXTURE, which was highly consistent with their geographical distribution. Two models for genome-wide association studies (GWAS), namely restricted two-stage multi-locus multi-allele (RTM)-GWAS and mixed linear model (MLM)-GWAS, were used to dissect the genetic architecture of the agronomic traits based on 13,723 SNPs. Eleven quantitative trait loci (QTLs) for seven traits were identified using two models (RTM- and MLM-GWAS). Among them, five were considered stable QTLs that were identified in at least two environments using MLM-GWAS. One putative candidate gene (SETIT_006045mg, Chr4: 744,701-746,852) that can enhance grain weight per panicle was identified based on homologous gene comparison and gene expression analysis and was validated by haplotype analysis of 330 accessions with high-depth (10×) resequencing data (unpublished). In addition, homologous gene comparison and haplotype analysis identified one putative foxtail millet ortholog (SETIT_032906mg, Chr2: 5,020,600-5,029,771) with rice affecting the target traits. Two markers (cGWP6045 and kTGW2906) were developed and validated and can be used for marker-assisted selection of foxtail millet with high grain weight. The results provide a fundamental resource for foxtail millet genetic research and breeding and demonstrate the power of integrating RTM- and MLM-GWAS approaches as a complementary strategy for investigating complex traits in foxtail millet.

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

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

Integration of genetic and genomics resources in einkorn wheat enables precision mapping of important traits

Einkorn wheat (Triticum monococcum) is an ancient grain crop and a close relative of the diploid progenitor (T. urartu) of polyploid wheat. It is the only diploid wheat species having both domesticated and wild forms and therefore provides an excellent system to identify domestication genes and genes for traits of interest to utilize in wheat improvement. Here, we leverage genomic advancements for einkorn wheat using an einkorn reference genome assembly combined with skim-sequencing of a large genetic population of 812 recombinant inbred lines (RILs) developed from a cross between a wild and a domesticated T. monococcum accession. We identify 15,919 crossover breakpoints delimited to a median and average interval of 114 Kbp and 219 Kbp, respectively. This high-resolution mapping resource enables us to perform fine-scale mapping of one qualitative (red coleoptile) and one quantitative (spikelet number per spike) trait, resulting in the identification of small physical intervals (400 Kb to 700 Kb) with a limited number of candidate genes. Furthermore, an important domestication locus for brittle rachis is also identified on chromosome 7A. This resource presents an exciting route to perform trait discovery in diploid wheat for agronomically important traits and their further deployment in einkorn as well as tetraploid pasta wheat and hexaploid bread wheat cultivars.

Gene-allele system of shade tolerance in southern China soybean germplasm revealed by genome-wide association study using gene-allele sequence as markers

KEY MESSAGE

Fifty-three shade tolerance genes with 281 alleles in the SCSGP were identified directly using gene-allele sequence as markers in RTM GWAS, from which optimized crosses, evolutionary motivators, and gene-allele networks were explored. Shade tolerance is a key for optimal cultivation of soybean inter/relay-cropped with corn. To explore the shade tolerance gene-allele system in the southern China soybean germplasm, we proposed using gene-allele sequence markers (GASMs) in a restricted two-stage multi-locus model genome-wide association study (GASM-RTM-GWAS). A representative sample with 394 accessions was tested for their shade tolerance index (STI), in Nanning, China. Through whole-genome re-sequencing, 47,586 GASMs were assembled. From GASM-RTM-GWAS, 53 main-effect STI genes with 281 alleles (2-13 alleles/gene) (totally 63 genes with 308 alleles, including 38 G × E genes with 191 alleles) were identified and then organized into a gene-allele matrix composed of eight submatrices corresponding to geo-seasonal subpopulations. The population featured mild STI changes (1.69 → 1.56-1.82) and mild gene-allele changes (92.5% alleles inherited, 0% alleles excluded, 7.5% alleles emerged) from the primitive (SA_III) to the derived seven subpopulations, but large transgressive recombination potentials and optimal crosses were predicted. The 63 STI genes were annotated into six biological categories (metabolic process, catalytic activity, response to stresses, transcription and translation, signal transduction and transport and unknown functions), interacted as gene networks. From the STI gene-allele system, 38 important alleles of 22 genes were nominated for further in-depth study. GASM-RTM-GWAS performed powerful and efficient in germplasm population genetic study comparing to other procedures through facilitating direct and thorough identification of its gene-allele system, from which genome-wide breeding by design could be achieved, and evolutionary motivators and gene-allele networks could be explored.

An Improved Genome-Wide Association Procedure Explores Gene-Allele Constitutions and Evolutionary Drives of Growth Period Traits in the Global Soybean Germplasm Population

In soybeans (Glycine max (L.) Merr.), their growth periods, DSF (days of sowing-to-flowering), and DFM (days of flowering-to-maturity) are determined by their required accumulative day-length (ADL) and active temperature (AAT). A sample of 354 soybean varieties from five world eco-regions was tested in four seasons in Nanjing, China. The ADL and AAT of DSF and DFM were calculated from daily day-lengths and temperatures provided by the Nanjing Meteorological Bureau. The improved restricted two-stage multi-locus genome-wide association study using gene-allele sequences as markers (coded GASM-RTM-GWAS) was performed. (i) For DSF and its related ADL_DSF and AAT_DSF, 130-141 genes with 384-406 alleles were explored, and for DFM and its related ADL_DFM and AAT_DFM, 124-135 genes with 362-384 alleles were explored, in a total of six gene-allele systems. DSF shared more ADL and AAT contributions than DFM. (ii) Comparisons between the eco-region gene-allele submatrices indicated that the genetic adaptation from the origin to the geographic sub-regions was characterized by allele emergence (mutation), while genetic expansion from primary maturity group (MG)-sets to early/late MG-sets featured allele exclusion (selection) without allele emergence in addition to inheritance (migration). (iii) Optimal crosses with transgressive segregations in both directions were predicted and recommended for breeding purposes, indicating that allele recombination in soybean is an important evolutionary drive. (iv) Genes of the six traits were mostly trait-specific involved in four categories of 10 groups of biological functions. GASM-RTM-GWAS showed potential in detecting directly causal genes with their alleles, identifying differential trait evolutionary drives, predicting recombination breeding potentials, and revealing population gene networks.

The Identification of a Yield-Related Gene Controlling Multiple Traits Using GWAS in Sorghum (Sorghum bicolor L.)

Sorghum bicolor (L.) is one of the oldest crops cultivated by human beings which has been used in food and wine making. To understand the genetic diversity of sorghum breeding resources and further guide molecular-marker-assisted breeding, six yield-related traits were analyzed for 214 sorghum germplasm from all over the world, and 2,811,016 single-nucleotide polymorphisms (SNPs) markers were produced by resequencing these germplasms. After controlling Q and K, QTLs were found to be related to the traits using three algorisms. Interestingly, an important QTL was found which may affect multiple traits in this study. It was the most likely candidate gene for the gene SORBI_3008G116500, which was a homolog of Arabidopsis thaliana gene-VIP5 found by analyzing the annotation of the gene in the LD block. The haplotype analysis showed that the SORBI_3008G116500^hap3 was the elite haplotype, and it only existed in Chinese germplasms. The traits were proven to be more associated with the SNPs of the SORBI_3008G116500 promoter through gene association studies. Overall, the QTLs and the genes identified in this study would benefit molecular-assisted yield breeding in sorghum.

Genome-Wide Association Studies (GWAS)

Most of the breeding targets are quantitative traits. In exploring the quantitative trait locus (QTL) system of a trait, linkage mapping was established using sparse polymerase chain reaction (PCR) markers. With the genome-wide sequencing technology advanced, genome-wide association study (GWAS) was developed for natural (germplasm) populations using dense genomic markers, which facilitates the identification of the complete QTL system with their multiple alleles on genomic locations. GWAS makes use of the linkage disequilibrium (LD) due to historical saturate recombination and high-density genomic markers to detect QTLs through statistical test for the association between molecular markers and phenotypes. However, due to inbreeding and mixture of source populations, the germplasm population often has complex and unknown structure, which leads to false positives/negatives in GWAS. Various GWAS methods have been proposed to reduce false positives/negatives, including those of the general linear model and the mixed linear model, which focused mainly on finding a handful of major QTLs under single-locus model for major gene cloning and could not detect directly the multiple alleles using bi-allelic single-nucleotide polymorphism (SNP) marker. As a relatively thorough detection of QTLs with their multiple alleles is required for germplasm population, the restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS) procedure was proposed for identifying the QTL system with varying multiple alleles. From the RTM-GWAS results, a QTL-allele matrix is constructed as a compact form of the population genetic constitution, which can be further used for crop genetic and breeding studies, including major gene mining, population evolution, and breeding by genetic design.

Mapping Locus RSC11K and predicting candidate gene resistant to Soybean mosaic virus strain SC11 through linkage analysis combined with genome resequencing of the parents in soybean

Soybean mosaic virus (SMV) strain SC11 was prevalent in middle China. Its resistance was controlled by a Mendelian single dominant gene RSC11K in soybean Kefeng-1. This study aimed at mapping RSC11K and identifying its candidate gene. RSC11K locus was mapped ~217 kb interval between two SNP-linkage-disequilibrium-blocks (Gm02_BLOCK_11273955_11464884 and Gm02_BLOCK_11486875_11491354) in W82.a1.v1 genome using recombinant inbred lines population derived from Kefeng-1 (Resistant) × NN1138-2 (Susceptible), but inserted with a ~245 kb segment in W82.a2.v1 genome. In the entire 462 kb RSC11K region, 429 SNPs, 142 InDels and 34 putative genes were identified with more SNPs/InDels distributed in non-functional regions. Thereinto, ten genes contained SNP/InDel variants with high and moderate functional impacts on proteins, among which Glyma.02G119700 encoded a typical innate immune receptor-like kinase involving in virus disease process and responded to SMV inoculation, therefore was recognized as RSC11K's candidate gene. The novel RSC11K locus and candidate genes may help developing SMV resistance germplasm.

A study using single-locus and multi-locus genome-wide association study to identify genes associated with teat number in Hu sheep

The multiple teats trait is common in many species of mammals and is considered related to lactation ability in swine. However, in Hu sheep, related gene research is still relatively limited. In this study, a genome‐wide association study was used to identify genetic markers and genes related to the number of teats in the Hu sheep population, a native Chinese sheep breed. A single marker method and several multi‐locus methods were utilized. A total of 61 SNPs were found to be related to the number of teats. Among these, 11 SNPs and one SNP were consistently detected by two and three multi‐locus models respectively. Four SNPs were concordantly identified between the single marker and multi‐locus methods. We also performed quantitative real‐time PCR testing of these identified candidate genes, identifying three genes with significantly different expression. Our study suggested that the LHFP, DPYSL2, and TDP‐43 genes may be related to the number of teats in sheep. The combination of single and multi‐locus GWAS detected additional SNPs not found with only one model. Our results provide new and important insights into the genetic mechanisms of the mammalian multiparous teat phenotype. These findings may be useful for future breeding and understanding the genetics of sheep and other livestock.

Characterization of Root System Architecture Traits in Diverse Soybean Genotypes Using a Semi-Hydroponic System

Phenotypic variation and correlations among root traits form the basis for selecting and breeding soybean varieties with efficient access to water and nutrients and better adaptation to abiotic stresses. Therefore, it is important to develop a simple and consistent system to study root traits in soybean. In this study, we adopted the semi-hydroponic system to investigate the variability in root morphological traits of 171 soybean genotypes popularized in the Yangtze and Huaihe River regions, eastern China. Highly diverse phenotypes were observed: shoot height (18.7-86.7 cm per plant with a median of 52.3 cm); total root length (208-1663 cm per plant with a median of 885 cm); and root mass (dry weight) (19.4-251 mg per plant with a median of 124 mg). Both total root length and root mass exhibited significant positive correlation with shoot mass (p ≤ 0.05), indicating their relationship with plant growth and adaptation strategies. The nine selected traits contributed to one of the two principal components (eigenvalues > 1), accounting for 78.9% of the total genotypic variation. Agglomerative hierarchical clustering analysis separated the 171 genotypes into five major groups based on these root traits. Three selected genotypes with contrasting root systems were validated in soil-filled rhizoboxes (1.5 m deep) until maturity. Consistent ranking of the genotypes in some important root traits at various growth stages between the two experiments indicates the reliability of the semi-hydroponic system in phenotyping root trait variability at the early growth stage in soybean germplasms.

Uncovering the Novel QTLs and Candidate Genes of Salt Tolerance in Rice with Linkage Mapping, RTM-GWAS, and RNA-seq

Salinity is a major abiotic stress that limits plant growth and crop productivity. Indica rice and japonica rice show significant differences in tolerance to abiotic stress, and it is considered a feasible method to breed progeny with stronger tolerance to abiotic stress by crossing indica and japonica rice. We herein developed a high-generation recombinant inbred lines (RILs) from Luohui 9 (indica) X RPY geng (japonica). Based on the high-density bin map of this RILs population, salt tolerance QTLs controlling final survival rates were analyzed by linkage mapping and RTM-GWAS methods. A total of seven QTLs were identified on chromosome 3, 4, 5, 6, and 8. qST-3.1, qST-5.1, qST-6.1, and qST-6.2 were novel salt tolerance QTLs in this study and their function were functionally verified by comparative analysis of parental genotype RILs. The gene aggregation result of these four new QTLs emphasized that the combination of the four QTL synergistic genotypes can significantly improve the salt stress tolerance of rice. By comparing the transcriptomes of the root tissues of the parents' seedlings, at 3 days and 7 days after salt treatment, we then achieved fine mapping of QTLs based on differentially expressed genes (DEGs) identification and DEGs annotations, namely, LOC_Os06g01250 in qST-6.1, LOC_Os06g37300 in qST-6.2, LOC_Os05g14880 in qST-5.1. The homologous genes of these candidate genes were involved in abiotic stress tolerance in different plants. These results indicated that LOC_Os05g14880, LOC_Os06g01250, and LOC_Os06g37300 were the candidate genes of qST-5.1, qST-6.1, and qST-6.2. Our finding provided novel salt tolerance-related QTLs, candidate genes, and several RILs with better tolerance, which will facilitate breeding for improved salt tolerance of rice varieties and promote the exploration tolerance mechanisms of rice salt stress.

Genome-Wide Association Study Identifies Genomic Regions for Important Morpho-Agronomic Traits in Mesoamerican Common Bean

The population growth trend in recent decades has resulted in continuing efforts to guarantee food security in which leguminous plants, such as the common bean (Phaseolus vulgaris L.), play a particularly important role as they are relatively cheap and have high nutritional value. To meet this demand for food, the main target for genetic improvement programs is to increase productivity, which is a complex quantitative trait influenced by many component traits. This research aims to identify Quantitative Trait Nucleotides (QTNs) associated with productivity and its components using multi-locus genome-wide association studies. Ten morpho-agronomic traits [plant height (PH), first pod insertion height (FPIH), number of nodules (NN), pod length (PL), total number of pods per plant (NPP), number of locules per pod (LP), number of seeds per pod (SP), total seed weight per plant (TSW), 100-seed weight (W100), and grain yield (YLD)] were evaluated in four environments for 178 Mesoamerican common bean domesticated accessions belonging to the Brazilian Diversity Panel. In order to identify stable QTNs, only those identified by multiple methods (mrMLM, FASTmrMLM, pLARmEB, and ISIS EM-BLASSO) or in multiple environments were selected. Among the identified QTNs, 64 were detected at least thrice by different methods or in different environments, and 39 showed significant phenotypic differences between their corresponding alleles. The alleles that positively increased the corresponding traits, except PH (for which lower values are desired), were considered favorable alleles. The most influenced trait by the accumulation of favorable alleles was PH, showing a 51.7% reduction, while NN, TSW, YLD, FPIH, and NPP increased between 18 and 34%. Identifying QTNs in several environments (four environments and overall adjusted mean) and by multiple methods reinforces the reliability of the associations obtained and the importance of conducting these studies in multiple environments. Using these QTNs through molecular techniques for genetic improvement, such as marker-assisted selection or genomic selection, can be a strategy to increase common bean production.

Linkage and association study discovered loci and candidate genes for glycinin and β-conglycinin in soybean (Glycine max L. Merr.)

KEY MESSAGE

Linkage mapping and GWAS identified 67 QTLs related to soybean glycinin, β-conglycinin and relevant traits. Polymorphisms of the candidate gene Gy1 promoter were associated with the glycinin content in soybean. The major components of storage proteins in soybean seeds are glycinin and β-conglycinin, which play important roles in determining protein nutrition and soy food processing properties. Increasing the protein content while improving the ratio of glycinin to β-conglycinin is substantially important for soybean protein improvement. To investigate the genetic mechanism of storage protein subunits, 184 recombinant inbred lines (RILs) derived from a cross of Kefeng No. 1 and Nannong 1138-2 and 211 diverse soybean cultivars were used to detect loci related to glycinin (11S), β-conglycinin (7S), the sum of glycinin and β-conglycinin (SGC), and the ratio of glycinin to β-conglycinin (RGC). Sixty-seven QTLs and 11 hot genomic regions were identified as affecting the four traits. One genetic region (q10-1) on chromosome 10 was associated with multiple traits by both linkage and association analysis. Eight genes in 11 hot genomic regions might be related to soybean protein subunit. The candidate gene analysis showed that polymorphisms in Gy1 promoters were significantly correlated with the 11S content. The QTLs and candidate genes identified in the present study allow for further understanding the genetic basis of 11S and 7S regulation and provide useful information for marker-assisted selection (MAS) in soybean quality improvement.

Evolutionary QTL-allele changes in main stem node number among geographic and seasonal subpopulations of Chinese cultivated soybeans

The main stem node number (MSN) is a trait related to geographic adaptation, plant architecture and yield potential of soybean. The QTL-allele constitution of the Chinese Cultivated Soybean Population (CCSP) was identified using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) procedure, from which a QTL-allele matrix was established and then separated into submatrices to explore the genetic structure, evolutionary differentiation, breeding potential and candidate genes of MSN in CCSP. The MSN of 821 accessions varied from 8.8 to 31.1, with an average of 16.3 in Nanjing, China (32.07° N, 118.62° E), where the MSNs of all the materials could be evaluated in a standardized manner. Among the six geo-seasonal subpopulations, the MSN varied from 21.7 in a southern summer-autumn-sowing subpopulation (SA-IV) down to 13.5 in a northeastern spring-sowing subpopulation (SP-I). The materials were genotyped with restriction site-associated DNA-sequencing. Totally 142 main-effect QTLs (73.24% new) with 560 alleles contributing 72.98% to the phenotypic variance were identified. The evolutionary QTL-allele changes in MSN from SA-IV through SP-I showed that inheritance (78.93% of alleles) was the primary factor influencing the evolution of this trait, followed by allele emergence (19.64% alleles), allele exclusion (1.43% alleles), and recombination among retained alleles. In the evolutionary changes, 70 QTLs, including 12 newly emerged QTLs, with 118 alleles were involved. An increase potential of 2-8 nodes was predicted and 112 candidate genes were annotated and preliminarily verified with χ2-tests in the CCSP. The RTM-GWAS showed powerful in detecting QTL-allele system, assessing evolution factors, predicting optimal crosses and identifying candidate genes in a germplasm population.

Identifying Wild Versus Cultivated Gene-Alleles Conferring Seed Coat Color and Days to Flowering in Soybean

Annual wild soybean (G. soja) is the ancestor of the cultivated soybean (G. max). To reveal the genetic changes from soja to max, an improved wild soybean chromosome segment substitution line (CSSL) population, SojaCSSLP5, composed of 177 CSSLs with 182 SSR markers (SSR-map), was developed based on SojaCSSLP1 generated from NN1138-2(max)×N24852(soja). The SojaCSSLP5 was genotyped further through whole-genome resequencing, resulting in a physical map with 1366 SNPLDBs (SNP linkage-disequilibrium blocks), which are composed of more markers/segments, shorter marker length and more recombination breakpoints than the SSR-map and caused 721 new wild substituted segments. Using the SNPLDB-map, two loci co-segregating with seed-coat color (SCC) and six loci for days to flowering (DTF) with 88.02% phenotypic contribution were identified. Integrated with parental RNA-seq and DNA-resequencing, two SCC and six DTF candidate genes, including three previously cloned (G, E2 and GmPRR3B) and five newly detected ones, were predicted and verified at nucleotide mutant level, and then demonstrated with the consistency between gene-alleles and their phenotypes in SojaCSSLP5. In total, six of the eight genes were identified with the parental allele-pairs coincided to those in 303 germplasm accessions, then were further demonstrated by the consistency between gene-alleles and germplasm phenotypes. Accordingly, the CSSL population integrated with parental DNA and RNA sequencing data was demonstrated to be an efficient platform in identifying candidate wild vs. cultivated gene-alleles.

Identification of Additive-Epistatic QTLs Conferring Seed Traits in Soybean Using Recombinant Inbred Lines

Seed weight and shape are important agronomic traits that affect soybean quality and yield. In the present study, we used image analysis software to evaluate 100-seed weight and seed shape traits (length, width, perimeter, projection area, length/width, and weight/projection area) of 155 novel recombinant inbred soybean lines (NJRISX) generated by crossing "Su88-M21" and "XYXHD". We examined quantitative trait loci (QTLs) associated with the six traits (except seed weight per projection area), and identified 42 additive QTLs (5-8 QTLs per trait) accounting for 24.9-37.5% of the phenotypic variation (PV). Meanwhile, 2-4 epistatic QTL pairs per trait out of a total of 18 accounted for 2.5-7.2% of the PV; and unmapped minor QTLs accounted for the remaining 35.0-56.7% of the PV. A total of 28 additive and 11 epistatic QTL pairs were concentrated in nine joint QTL segments (JQSs), indicating that QTLs associated with seed weight and shape are closely related and interacted. An interaction was also detected between additive and epistatic QTL pairs and environment, which made significant contributions of 1.4-9.5% and 0.4-0.8% to the PV, respectively. We annotated 18 candidate genes in the nine JQSs, which were important for interpreting the close relationships among the six traits. These findings indicate that examining the interactions between closely related traits rather than only analyzing individual trait provides more useful insight into the genetic system of the interrelated traits for which there has been limited QTL information.

The Modification of Circadian Clock Components in Soybean During Domestication and Improvement

Agricultural production is greatly dependent on daylength, which is determined by latitude. Living organisms align their physiology to daylength through the circadian clock, which is made up of input sensors, core and peripheral clock components, and output. The light/dark cycle is the major input signal, moderated by temperature fluctuations and metabolic changes. The core clock in plants functions mainly through a number of transcription feedback loops. It is known that the circadian clock is not essential for survival. However, alterations in the clock components can lead to substantial changes in physiology. Thus, these clock components have become the de facto targets of artificial selection for crop improvement during domestication. Soybean was domesticated around 5,000 years ago. Although the circadian clock itself is not of particular interest to soybean breeders, specific alleles of the circadian clock components that affect agronomic traits, such as plant architecture, sensitivity to light/dark cycle, flowering time, maturation time, and yield, are. Consequently, compared to their wild relatives, cultivated soybeans have been bred to be more adaptive and productive at different latitudes and habitats for acreage expansion, even though the selection processes were made without any prior knowledge of the circadian clock. Now with the advances in comparative genomics, known modifications in the circadian clock component genes in cultivated soybean have been found, supporting the hypothesis that modifications of the clock are important for crop improvement. In this review, we will summarize the known modifications in soybean circadian clock components as a result of domestication and improvement. In addition to the well-studied effects on developmental timing, we will also discuss the potential of circadian clock modifications for improving other aspects of soybean productivity.

Genetic dynamics of earlier maturity group emergence in south-to-north extension of Northeast China soybeans

KEY MESSAGE

This population genetic study is characterized with direct comparisons of days to flowering QTL-allele matrices between newly evolved and originally old maturity groups of soybeans to explore its evolutionary dynamics using the RTM-GWAS procedure. The Northeast China (NEC) soybeans are the major germplasm source of modern soybean production in Americas (> 80% of the world total). NEC is a relatively new soybean area in China, expanded after its nomadic status in the seventeenth century. At nine sites of four ecoregions in NEC, 361 varieties were tested for their days to flowering (DTF), a geography-sensitive trait as an indicator for maturity groups (MGs). The DTF reduced obviously along with soybeans extended to higher latitudes, ranging in 41-83 days and MG 000-III. Using the RTM-GWAS (restricted two-stage multi-locus model genome-wide association study) procedure, 81 QTLs with 342 alleles were identified, accounting for 77.85% genetic contribution (R2 = 0.01-7.74%/locus), and other 20.75% (98.60-77.85%, h2 = 98.60%) genetic variation was due to a collective of unmapped QTLs. With soybeans northward, breeding effort made the original MG I-III evolved to MG 0-00-000. In direct comparisons of QTL-allele matrices among MGs, the genetic dynamics are identified with local exotic introduction/migration (58.48%) as the first and selection against/exclusion of positive alleles causing new recombination (40.64%) as the second, while only a few allele emergence/mutation happened (0.88%, limited in MG 0, not in MG 00-000). In new MG emergence, 24 QTLs with 19 candidate genes are the major sources. A genetic potential of further DTF shortening (13-21 days) is predicted for NEC population. The QTL detection in individual ecoregions showed various ecoregion-specific QTLs-alleles/genes after co-localization treatment (removing the random environment shifting ones).

Genomic Prediction Accuracy of Seven Breeding Selection Traits Improved by QTL Identification in Flax

Molecular markers are one of the major factors affecting genomic prediction accuracy and the cost of genomic selection (GS). Previous studies have indicated that the use of quantitative trait loci (QTL) as markers in GS significantly increases prediction accuracy compared with genome-wide random single nucleotide polymorphism (SNP) markers. To optimize the selection of QTL markers in GS, a set of 260 lines from bi-parental populations with 17,277 genome-wide SNPs were used to evaluate the prediction accuracy for seed yield (YLD), days to maturity (DTM), iodine value (IOD), protein (PRO), oil (OIL), linoleic acid (LIO), and linolenic acid (LIN) contents. These seven traits were phenotyped over four years at two locations. Identification of quantitative trait nucleotides (QTNs) for the seven traits was performed using three types of statistical models for genome-wide association study: two SNP-based single-locus (SS), seven SNP-based multi-locus (SM), and one haplotype-block-based multi-locus (BM) models. The identified QTNs were then grouped into QTL based on haplotype blocks. For all seven traits, 133, 355, and 1208 unique QTL were identified by SS, SM, and BM, respectively. A total of 1420 unique QTL were obtained by SS+SM+BM, ranging from 254 (OIL, LIO) to 361 (YLD) for individual traits, whereas a total of 427 unique QTL were achieved by SS+SM, ranging from 56 (YLD) to 128 (LIO). SS models alone did not identify sufficient QTL for GS. The highest prediction accuracies were obtained using single-trait QTL identified by SS+SM+BM for OIL (0.929 ± 0.016), PRO (0.893 ± 0.023), YLD (0.892 ± 0.030), and DTM (0.730 ± 0.062), and by SS+SM for LIN (0.837 ± 0.053), LIO (0.835 ± 0.049), and IOD (0.835 ± 0.041). In terms of the number of QTL markers and prediction accuracy, SS+SM outperformed other models or combinations thereof. The use of all SNPs or QTL of all seven traits significantly reduced the prediction accuracy of traits. The results further validated that QTL outperformed high-density genome-wide random markers, and demonstrated that the combined use of single and multi-locus models can effectively identify a comprehensive set of QTL that improve prediction accuracy, but further studies on detection and removal of redundant or false-positive QTL to maximize prediction accuracy and minimize the number of QTL markers in GS are warranted.

Dissecting the Genetic Architecture of Seed Protein and Oil Content in Soybean from the Yangtze and Huaihe River Valleys Using Multi-Locus Genome-Wide Association Studies

Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein and oil content are two valuable quality traits controlled by multiple genes in soybean. In this study, the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) procedure was performed to dissect the genetic architecture of seed protein and oil content in a diverse panel of 279 soybean accessions from the Yangtze and Huaihe River Valleys in China. We identified 26 quantitative trait loci (QTLs) for seed protein content and 23 for seed oil content, including five associated with both traits. Among these, 39 QTLs corresponded to previously reported QTLs, whereas 10 loci were novel. As reported previously, the QTL on chromosome 20 was associated with both seed protein and oil content. This QTL exhibited opposing effects on these traits and contributed the most to phenotype variation. From the detected QTLs, 55 and 51 candidate genes were identified for seed protein and oil content, respectively. Among these genes, eight may be promising candidate genes for improving soybean nutritional quality. These results will facilitate marker-assisted selective breeding for soybean protein and oil content traits.