Four-way Recombinant Inbred Line Research Articles

QTL for Main Stem Node Number and Its Response to Plant Densities in 144 Soybean FW-RILs.

Although the main stem node number of soybean [Glycine max (L.) Merr. ] is an important yield-related trait, there have been limited studies on the effect of plant density on the identification of quantitative trait loci (QTL) for main stem node number (MSNN). To address this issue, here, 144 four-way recombinant inbred lines (FW-RILs) derived from Kenfeng 14, Kenfeng 15, Heinong 48, and Kenfeng 19 were used to identify QTL for MSNN with densities of 2.2 × 105 (D1) and 3 × 105 (D2) plants/ha in five environments by linkage and association studies. As a result, the linkage and association studies identified 40 and 28 QTL in D1 and D2, respectively, indicating the difference in QTL in various densities. Among these QTL, five were common in the two densities; 36 were singly identified for response to density; 12 were repeatedly identified by both response to density and phenotype of two densities. Thirty-one were repeatedly detected across various methods, densities, and environments in the linkage and association studies. Among the 24 common QTL in the linkage and association studies, 15 explained a phenotypic variation of more than 10%. Finally, Glyma.06G094400, Glyma.06G147600, Glyma.19G160800.1, and Glyma.19G161100 were predicted to be associated with MSNN. These findings will help to elucidate the genetic basis of MSNN and improve molecular assistant selection in high-yield soybean breeding.

Fine mapping QTL and mining genes for protein content in soybean by the combination of linkage and association analysis.

Soybean is one main source of dietary protein; therefore, improving protein content is an important objective in breeding programs. There is a significant negative correlation between protein and oil content, which influenced mapping quantitative trait locus (QTL) and quantitative trait nucleotides for these two traits. In this study, a linkage map was created with 2232 single-nucleotide polymorphism markers for the four-way recombinant inbred line (FW-RIL) population derived from the cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19), and then conditional and unconditional QTL analyses were carried out by inclusive complete interval mapping based on the phenotypic data of protein and oil content collected in 10 different environments. As shown in the results of linkage analysis, a total of 85 QTL have been detected. We have performed association analysis using 109,676 markers after quality filtering for FW-RIL, and the results have shown that a total of 60 QTNs were detected. We have performed association analysis using 63,306 markers after quality filtering for resource population, and the results have shown that a total of 123 QTNs were detected. We have combined linkage and association analysis, and there are six QTNs verified by FW-RIL and resource population. We have performed pathway analysis on the genes in these six QTN attenuation regions, and the result shows that a total of four candidate genes are related to the synthesis or metabolism of soybean protein. These findings will facilitate marker-assisted selection and molecular breeding of soybean.

Detection of QTL and QTN and candidate genes for oil content in soybean using a combination of four-way-RIL and germplasm populations

Soybean is a source of edible oil for humans and provides a third of the vegetable oil consumed worldwide. Increasing seed oil content in seeds is thus a key objective in soybean breeding. In the present study, a four-way recombinant inbred line (FW-RIL) population comprising 144 lines, planted in 10 environments, and a germplasm panel of 455 accessions, planted in two environments, were used to collect oil-content phenotypes. First, 59 quantitative trait loci (QTL) were detected in the FW-RIL population by inclusive complete interval mapping on a linkage map consisting of 2232 single-nucleotide polymorphism (SNP) markers. Also in the FW-RILs, 44 quantitative trait nucleotides (QTNs) were detected by association analysis using 109,676 SNP markers and five methods of multi-locus genome-wide association study. Second, 77 QTN were detected by association analysis in the germplasm panel using 63,306 markers. Comparison of the QTL and QTN suggested four QTN controlling oil content. Pathway analysis was performed on genes in attenuation regions of these four QTN, and two candidate genes involved in the synthesis or metabolism of soybean oil were identified. These findings provide useful information about the genetics of oil content and may contribute to its genetic improvement by marker-assisted selection.

Identification of QTNs Controlling 100-Seed Weight in Soybean Using Multilocus Genome-Wide Association Studies.

Hundred-seed weight (HSW) is an important measure of yield and a useful indicator to monitor the inheritance of quantitative traits affected by genotype and environmental conditions. To identify quantitative trait nucleotides (QTNs) and mine genes useful for breeding high-yielding and high-quality soybean (Glycine max) cultivars, we conducted a multilocus genome-wide association study (GWAS) on HSW of soybean based on phenotypic data from 20 different environments and genotypic data for 109,676 single-nucleotide polymorphisms (SNPs) in 144 four-way recombinant inbred lines. Using five multilocus GWAS methods, we identified 118 QTNs controlling HSW. Among these, 31 common QTNs were detected by various methods or across multiple environments. Pathway analysis identified three potential candidate genes associated with HSW in soybean. We used allele information to study the common QTNs in 20 large-seed and 20 small-seed lines and identified a higher percentage of superior alleles in the large-seed lines than in small-seed lines. These observations will contribute to construct the gene networks controlling HSW in soybean, which can improve the genetic understanding of HSW, and provide assistance for molecular breeding of soybean large-seed varieties.

Quantitative Trait Locus Analysis of Protein and Oil Content in Response to Planting Density in Soybean (Glycine max [L.] Merri.) Seeds Based on SNP Linkage Mapping.

Soybean varieties suitable for high planting density allow greater yields. However, the seed protein and oil contents, which determine the value of this crop, can be influenced by planting density. Thus, it is important to understand the genetic basis of the responses of different soybean genotypes to planting density. In this study, we quantified the protein and oil contents in a four-way recombinant inbred line (FW-RIL) soybean population under two planting densities and the response to density. We performed quantitative trait locus (QTL) mapping using a single nucleotide polymorphism (SNP) linkage map generated by inclusive composite interval mapping. We identified 14 QTLs for protein content and 17 for oil content at a planting density of 2.15 × 105 plant/ha (D1) and 14 QTLs for protein content and 20 for oil content at a planting density 3.0 × 105 plant/ha (D2). Among the QTLs detected, two oil-content QTLs was detected at both plant densities. In addition, we identified 38 QTLs for the responses of protein and oil contents to planting density. Of the QTLs detected, 70 were identified in previous studies, while 33 were newly identified. Fourty-five QTLs accounted for over 10% of the phenotypic variation of the corresponding trait, based on 23 QTLs at a marker interval distance of ~600 kb detected under different densities and with the responses to density difference. Pathway analysis revealed four candidate genes involved in protein and oil biosynthesis/metabolism. These results improve our understanding of the genetic underpinnings of protein and oil biosynthesis in soybean, laying the foundation for enhancing protein and oil contents and increasing yields in soybean.

Identification of QTL and genes for pod number in soybean by linkage analysis and genome-wide association studies

Pod number is an important component of yield in soybean (Glycine max (L.) Merr.), and pods are unevenly distributed in the upper, middle and lower parts of the plant. Here, linkage analysis combined with genome-wide association study (GWAS) was used to identify 20 pod number-related traits in four-way recombinant inbred lines (FW-RILs) derived from crosses between the four soybean varieties (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19). The results show that pod number-related traits are highly influenced by genetic factors, while the environment plays only a minor role. A total of 602 QTLs were identified, of which 52 were detected in multiple environments (over two environments). In addition, GWAS detected 26 common QTNs (identified by multiple methods or environments) in the QTL intervals. Based on gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, 11 potential candidate genes were identified that likely involved the growth and development of soybean pods. These findings will help elucidate the genetic basis of pod number traits.

Mapping QTLs for protein and oil content in soybean by removing the influence of related traits in a four-way recombinant inbred line population – ERRATUM

Mapping QTLs for protein and oil content in soybean by removing the influence of related traits in a four-way recombinant inbred line population – ERRATUM

Mapping QTLs for protein and oil content in soybean by removing the influence of related traits in a four-way recombinant inbred line population

AbstractProtein content (PC) and oil content (OC) are important breeding traits of soybean [Glycine max(L.) Merr.]. Quantitative trait locus (QTL) mapping for PC and OC is important for molecular breeding in soybean; however, the negative correlation between PC and OC influences the accuracy of QTL mapping. In the current study, a four-way recombinant inbred lines (FW-RILs) population comprising 160 lines derived from the cross (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19) was planted in eight different environments and PC and OC measured. Conditional and unconditional QTL analyses were carried out by interval mapping (IM) and inclusive complete IM based on linkage maps of 275 simple sequences repeat markers in a FW-RILs population. This analysis revealed 59 unconditional QTLs and 52 conditional QTLs among the FW-RILs. An analysis of additive effects indicated that the effects of 13 protein QTLs were not related to OC, whereas OC affected the expression of 13 and eight QTLs either partially or completely, respectively. Eight QTLs affecting OC were not influenced by PC, whereas six and 26 QTLs were partially and fully affected by PC, respectively. Among the QTLs detected in the current study, two protein QTLs and five oil QTLs had not been previously reported. These findings will facilitate marker-assisted selection and molecular breeding of soybean.

Mapping developmental QTL for plant height in soybean [Glycine max (L.) Merr.] using a four-way recombinant inbred line population.

Plant height (PH) is an important trait in soybean, as taller plants may have higher yields but may also be at risk for lodging. Many genes act jointly to influence PH throughout development. To map the quantitative trait loci (QTL) controlling PH, we used the unconditional variable method (UVM) and conditional variable method (CVM) to analyze PH data for a four-way recombinant inbred line (FW-RIL) population derived from the cross of (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19). We identified 7, 8, 16, 19, 15, 27, 17, 27, 22, and 24 QTL associated with PH at 10 developmental stages, respectively. These QTL mapped to 95 genomic regions. Among these QTL, 9 were detected using UVM and CVM, and 89 and 66 were only detected by UVM or CVM, respectively. In total, 36 QTL controlling PH were detected at multiple developmental stages and these made unequal contributions to genetic variation throughout development. Among 19 novel regions discovered in our study, 7 could explain over 10% of the phenotypic variation and contained only one single QTL. The unconditional and conditional QTL detected here could be used in molecular design breeding across the whole developmental procedure.

Mapping QTL affecting the vertical distribution and seed set of soybean [Glycine max (L.) Merr.] pods

Number of pods per plant and number of seeds per pod are quantitative, multigenic traits and important components of yield in soybean [Glycine max (L.) Merr.]. Pods are distributed unevenly in the upper, middle, and lower segments of the plant and this distribution is affected by sowing date (SD). A population of four-way recombinant inbred lines (FW-RIL), containing 160 F2:8 individuals, was generated from the cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19). A linkage map consisting of 275 simple sequence repeat (SSR) markers was used to map quantitative trait loci (QTL) associated with the production of one, two, three, and four seeds per pod in the upper, middle, and lower segments of plants at two SDs, totaling 12 measurements per SD. A wide range of variation in the twelve characteristics was observed among the four parental lines and the FW-RIL population at the two SDs. The effect of SD2 (May 17, 2016) on pod number was stronger than that of SD1 (May 7, 2016) because the heritability of each trait in the SD1 experiment was generally greater than that of SD2. The study identified 76 QTL controlling pod number, with the phenotypic variation explained by each QTL ranging from 1.86% to 13.71%. The numbers of QTL controlling one, two, three, and four seeds per pod were 28, 23, 23, and 23, respectively. There were 30, 28, and 28 QTL controlling the pod number in the upper, middle, and lower segments of the plant, respectively. Forty-five QTL were identified at SD1 and 38 QTL were identified at SD2. Seventeen QTL were associated with pod-number traits. The QTL qPN-A1–3 was associated with the number of pods containing one seed in the middle segment of the plant at both SDs. Sixty-three QTL were published QTL (common areas existed when integrating on a map GmComposite2003 of Wm82 based on left and right markers). and 13 QTL related to pod number were newly discovered. These results provide a reference for breeders to improve soybean yield by combining advantageous alleles for these QTL. Future studies may reveal candidate genes for these QTL and identify causal alleles for marker-assisted selection.

Identification of QTNs Controlling Seed Protein Content in Soybean Using Multi-Locus Genome-Wide Association Studies.

Protein content (PC), an important trait in soybean (Glycine max) breeding, is controlled by multiple genes with relatively small effects. To identify the quantitative trait nucleotides (QTNs) controlling PC, we conducted a multi-locus genome-wide association study (GWAS) for PC in 144 four-way recombinant inbred lines (FW-RILs). All the FW-RILs were phenotyped for PC in 20 environments, including four locations over 4 years with different experimental treatments. Meanwhile, all the FW-RILs were genotyped using SoySNP660k BeadChip, producing genotype data for 109,676 non-redundant single-nucleotide polymorphisms. A total of 129 significant QTNs were identified by five multi-locus GWAS methods. Based on the 22 common QTNs detected by multiple GWAS methods or in multiple environments, pathway analysis identified 8 potential candidate genes that are likely to be involved in protein synthesis and metabolism in soybean seeds. Using superior allele information for 22 common QTNs in 22 elite and 7 inferior lines, we found higher superior allele percentages in the elite lines and lower percentages in the inferior lines. These findings will contribute to the discovery of the polygenic networks controlling PC in soybean, increase our understanding of the genetic foundation and regulation of PC, and be useful for molecular breeding of high-protein soybean varieties.

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population.

Mapping QTL Protein and Oil Contents Using Population from Four-way Re-combinant Inbred Lines for Soybean ( Glycine max L. Merr.)

Mapping QTL Protein and Oil Contents Using Population from Four-way Re-combinant Inbred Lines for Soybean ( <i>Glycine max</i> L. Merr.)

Bayesian QTL mapping for recombinant inbred lines derived from a four-way cross

Recombinant inbred lines (RILs) are created by a cross between inbred lines followed by repeated selfing or sib-mating, which include many different types of new inbred lines with many recombination events on the genome. The phenotype of each RIL can be assessed based on multiple individuals within the same line to reduce non-genetic variability. Therefore, RILs are useful tools for QTL mapping allowing effective detection and fine localization of QTL. Usually only two inbred lines are involved to create RILs. In such two-way RILs, however, there are at most two different alleles at QTL and only QTL segregating between two parental lines can be detected. Recently a new crossing design using multiple founders for creating RILs was proposed in Arabidopsis thaliana and mice, where the genome of each RIL is a mosaic of the genomes from multiple parents. Such multi-way RILs are more useful to improve the efficiency in QTL detection than two-way RILs because of the increased chance of QTL segregation among multiple lines, leading to the successful detection of many QTL. In this paper, we propose a Bayesian method for mapping multiple QTL simultaneously in four-way RILs allowing the inference about the equivalence relationship among the four possible alleles descended from the four founder lines as well as the estimation of QTL locations and effects via a MCMC algorithm. Simulation experiments show that our method has the practical ability to detect QTL and to provide the information of equivalence relationship among alleles at detected QTL using four-way RILs.