Epistatic Quantitative Trait Loci Research Articles

Linkage and association mapping in multi-parental populations reveal the genetic basis of carotenoid variation in maize kernels.

Carotenoids are indispensable to plants and critical components of the human diet. The carotenoid metabolic pathway is conserved across plant species, but our understanding of the genetic basis of carotenoid variation remains limited for the seeds of most cereal crops. To address this issue, we systematically performed linkage and association mapping for eight carotenoid traits using six recombinant inbred line (RIL) populations. Single linkage mapping (SLM) and joint linkage mapping (JLM) identified 77 unique additive QTLs and 104 pairs of epistatic QTLs. Among these QTLs, we identified 22 overlapping hotspots of additive and epistatic loci, highlighting the important contributions of some QTLs to carotenoid levels through additive or epistatic mechanisms. A genome-wide association study based on all RILs detected 244 candidate genes significantly associated with carotenoid traits, 23 of which were annotated as carotenoid pathway genes. Effect comparisons suggested that a small number of loci linked to pathway genes have substantial effects on carotenoid variation in our tested populations, but many loci not associated with pathway genes also make important contributions to carotenoid variation. We identified ZmPTOX as the causal gene for a QTL hotspot (Q10/JLM10/GWAS019); this gene encodes a putative plastid terminal oxidase that produces plastoquinone-9 used by two enzymes in the carotenoid pathway. Natural variants in the promoter and second exon of ZmPTOX were found to alter carotenoid levels. This comprehensive assessment of the genetic mechanisms underlying carotenoid variation establishes a foundation for rewiring carotenoid metabolism and accumulation for efficient carotenoid biofortification.

Quantitative trait loci detection for three tiller-related traits and the effects on wheat (Triticum aestivum L.) yields.

A total of 38 putative additive QTLs and 55 pairwise putative epistatic QTLs for tiller-related traits were reported, and the candidate genes underlying qMtn-KJ-5D, a novel major and stable QTL for maximum tiller number, were characterized. Tiller-related traits play an important role in determining the yield potential of wheat. Therefore, it is important to elucidate the genetic basis for tiller number when attempting to use genetic improvement as a tool for enhancing wheat yields. In this study, a quantitative trait locus (QTL) analysis of three tiller-related traits was performed on the recombinant inbred lines (RILs) of a mapping population, referred to as KJ-RILs, that was derived from a cross between the Kenong 9204 (KN9204) and Jing 411 (J411) lines. A total of 38 putative additive QTLs and 55 pairwise putative epistatic QTLs for spike number per plant (SNPP), maximum tiller number (MTN), and ear-bearing tiller rate (EBTR) were detected in eight different environments. Among these QTLs with additive effects, three major and stable QTLs were first documented herein. Almost all but two pairwise epistatic QTLs showed minor interaction effects accounting for no more than 3.0% of the phenotypic variance. The genetic effects of two colocated major and stable QTLs, i.e., qSnpp-KJ-5D.1 and qMtn-KJ-5D, for yield-related traits were characterized. The breeding selection effect of the beneficial allele for the two QTLs was characterized, and its genetic effects on yield-related traits were evaluated. The candidate genes underlying qMtn-KJ-5D were predicted based on multi-omics data, and TraesKN5D01HG00080 was identified as a likely candidate gene. Overall, our results will help elucidate the genetic architecture of tiller-related traits and can be used to develop novel wheat varieties with high yields.

Fine-Tuning Quantitative Trait Loci Identified in Immortalized F2 Population Are Essential for Genomic Prediction of Hybrid Performance in Maize

Maize breeding is greatly affected by hybrid vigor, a phenomenon that hybrids exhibit superior performance than parental lines. The immortalized F2 population (IMF2) is ideal for the genetic dissection and prediction of hybrid performance. Here, in this study, we conducted the QTL mapping and genomic prediction of six traits related to plant architecture using an IMF2 population. Broad-sense heritability of these traits ranged from 0.85 to 0.94. Analysis of genetic effects showed that additive variance was the main contributor to phenotypic variations. The mapping of quantitative trait loci (QTLs) revealed 10 to 16 QTLs (including pleiotropic loci and epistatic QTLs) for the six traits. Additionally, we identified 15 fine-tuning QTLs for plant height (PH). For genomic prediction (GP), the model of additive and dominance (AD) exhibited higher prediction accuracy than those fitting general combining ability (GCA) and its combination with special combining ability (SCA) effects for all tested traits. And adding the epistasis (E) effect into the AD model did not significantly increase its prediction accuracy. Moreover, the identified 15 fine-tuning QTLs of PH, which exerted large genomic prediction effects, were verified by the marker effect of GP. Our results not only provide an approach for the fine-mapping of fine-tuning QTLs but also serve as references for GP breeding in crops.

Genome-Wide Mapping of Quantitative Trait Loci for Yield-Attributing Traits of Peanut.

Peanuts (Arachis hypogaea L.) are important high-protein and oil-containing legume crops adapted to arid to semi-arid regions. The yield and quality of peanuts are complex quantitative traits that show high environmental influence. In this study, a recombinant inbred line population (RIL) (Valencia-C × JUG-03) was developed and phenotyped for nine traits under two environments. A genetic map was constructed using 1323 SNP markers spanning a map distance of 2003.13 cM. Quantitative trait loci (QTL) analysis using this genetic map and phenotyping data identified seventeen QTLs for nine traits. Intriguingly, a total of four QTLs, two each for 100-seed weight (HSW) and shelling percentage (SP), showed major and consistent effects, explaining 10.98% to 14.65% phenotypic variation. The major QTLs for HSW and SP harbored genes associated with seed and pod development such as the seed maturation protein-encoding gene, serine-threonine phosphatase gene, TIR-NBS-LRR gene, protein kinase superfamily gene, bHLH transcription factor-encoding gene, isopentyl transferase gene, ethylene-responsive transcription factor-encoding gene and cytochrome P450 superfamily gene. Additionally, the identification of 76 major epistatic QTLs, with PVE ranging from 11.63% to 72.61%, highlighted their significant role in determining the yield- and quality-related traits. The significant G × E interaction revealed the existence of the major role of the environment in determining the phenotype of yield-attributing traits. Notably, the seed maturation protein-coding gene in the vicinity of major QTLs for HSW can be further investigated to develop a diagnostic marker for HSW in peanut breeding. This study provides understanding of the genetic factor governing peanut traits and valuable insights for future breeding efforts aimed at improving yield and quality.

Identification of Putative Quantitative Trait Loci for Improved Seed Oil Quality in Peanuts.

Improving seed oil quality in peanut (Arachis hypogaea) has long been an aim of breeding programs worldwide. The genetic resources to achieve this goal are limited. We used an advanced recombinant inbred line (RIL) population derived from JH5 × KX01-6 to explore quantitative trait loci (QTL) affecting peanut oil quality and their additive effects, epistatic effects, and QTL × environment interactions. Gas chromatography (GC) analysis suggested seven fatty acids components were obviously detected in both parents and analyzed in a follow-up QTL analysis. The major components, palmitic acid (C16:0), oleic acid (C18:1), and linoleic acid (C18:2), exhibited considerable phenotypic variation and fit the two major gene and minor gene mixed-inheritance model. Seventeen QTL explained 2.57-38.72% of the phenotypic variation in these major components, with LOD values of 4.12-37.56 in six environments, and thirty-five QTL explained 0.94-32.21% of the phenotypic variation, with LOD values of 5.99-150.38 in multiple environments. Sixteen of these QTL were detected in both individual and multiple environments. Among these, qFA_08_1 was a novel QTL with stable, valuable and major effect. Two other major-effect QTL, qFA_09_2 and qFA_19_3, share the same physical position as FAD2A and FAD2B, respectively. Eleven stable epistatic QTL involving nine loci explained 1.30-34.97% of the phenotypic variation, with epistatic effects ranging from 0.09 to 6.13. These QTL could be valuable for breeding varieties with improved oil quality.

Genetic basis of maize stalk strength decoded via linkage and association mapping.

Stalk lodging is a severe problem that limits maize production worldwide, although little attention has been given to its genetic basis. Here we measured rind penetrometer resistance (RPR), an effective index for stalk lodging, in a multi-parent population of 1948 recombinant inbred lines (RILs) and an association population of 508 inbred lines (AMP508). Linkage and association mapping identified 53 and 29 single quantitative trait loci (QTLs) and 50 and 19 pairs of epistatic interactions for RPR in the multi-parent population and AMP508 population, respectively. Phenotypic variation explained by all identified epistatic QTLs (up to ~5%) was much less than that explained by all single additive QTLs (up to ~33% in the multi-parent population and ~ 60% in the AMP508 population). Among all detected QTLs, only eight single QTLs explained >10% of phenotypic variation in single RIL populations. Alleles that increased RPR were enriched in tropical/subtropical (TST) groups from the AMP508 population. Based on genome-wide association studies in both populations, we identified 137 candidate genes affecting RPR, which were assigned to multiple biological processes, such as the biosynthesis of cell wall components. Sixty-six candidate genes were cross-validated by multiple methods or populations. Most importantly, 23 candidate genes were upregulated or downregulated in high-RPR lines relative to low-RPR lines, supporting the associations between candidate genes and RPR. These findings reveal the complex nature of the genetic basis underlying RPR and provide loci or candidate genes for developing elite varieties that are resistant to stalk lodging via molecular breeding.

Novel quantitative trait loci from an interspecific Brassica rapa derivative improve pod shatter resistance in Brassica napus.

Pod shatter is a trait of agricultural relevance that ensures plants dehisce seeds in their native environment and has been subjected to domestication and selection for non-shattering types in several broadacre crops. However, pod shattering causes a significant yield reduction in canola (Brassica napus L.) crops. An interspecific breeding line BC95042 derived from a B. rapa/B. napus cross showed improved pod shatter resistance (up to 12-fold than a shatter-prone B. napus variety). To uncover the genetic basis and improve pod shatter resistance in new varieties, we analysed F2 and F2:3 derived populations from the cross between BC95042 and an advanced breeding line, BC95041, and genotyped with 15,498 DArTseq markers. Through genome scan, interval and inclusive composite interval mapping analyses, we identified seven quantitative trait loci (QTLs) associated with pod rupture energy, a measure for pod shatter resistance or pod strength, and they locate on A02, A03, A05, A09 and C01 chromosomes. Both parental lines contributed alleles for pod shatter resistance. We identified five pairs of significant epistatic QTLs for additive x additive, additive dominance and dominance x dominance interactions between A01/C01, A03/A07, A07/C03, A03/C03, and C01/C02 chromosomes for rupture energy. QTL effects on A03/A07 and A01/C01 were in the repulsion phase. Comparative mapping identified several candidate genes (AG, ABI3, ARF3, BP1, CEL6, FIL, FUL, GA2OX2, IND, LATE, LEUNIG, MAGL15, RPL, QRT2, RGA, SPT and TCP10) underlying main QTL and epistatic QTL interactions for pod shatter resistance. Three QTLs detected on A02, A03, and A09 were near the FUL (FRUITFULL) homologues BnaA03g39820D and BnaA09g05500D. Focusing on the FUL, we investigated putative motifs, sequence variants and the evolutionary rate of its homologues in 373 resequenced B. napus accessions of interest. BnaA09g05500D is subjected to purifying selection as it had a low Ka/Ks ratio compared to other FUL homologues in B. napus. This study provides a valuable resource for genetic improvement for yield through an understanding of the genetic mechanism controlling pod shatter resistance in Brassica species.

Dynamic QTL mapping revealed primarily the genetic structure of photosynthetic traits in castor (Ricinus communis L.)

High photosynthetic efficiency is the basis of high biomass and high harvest index in castor (Ricinus communis L.). Understanding the genetic law of photosynthetic traits will facilitate the breeding for high photosynthetic efficiency. In this study, the dynamic QTL mapping was performed with the populations F2 and BC1 derived from 2 parents with significant difference in net photosynthetic rate (Pn) at 3 stages, in order to reveal the genetic structure of photosynthetic traits. In F2 population, 26 single-locus QTLs were identified, including 3/3/1 (the QTL number at stage I/II/III, the same below), 1/2/0, 1/2/2, 1/3/1, 0/1/1, and 1/1/2 QTLs conferring Pn, water use efficiency (Wue), transpiration rate (Tr), stomatal conductance (Gs), intercellular CO2 concentration (Ci) and chlorophyll content (Cc), with a phenotypic variation explained (PVE) of 8.40%/8.91%/6.17%, 5.36%/31.74%/0, 7.31%/12.80%/15.15%, 1.60%/6.44%/0.02%, 0/1.10%/0.70% and 2.77%/3.96%/6.50% respectively. And 53 epistatic QTLs (31 pairs) were identified, including 2/2/5, 5/6/3, 4/4/2, 6/3/2, 3/2/0 and 4/0/0 ones conferring the above 6 traits, with a PVE of 6.52%/6.47%/19.04%, 16.72%/15.67%/14.12%, 18.57%/15.58%/7.34%, 21.72%/8.52%/7.13%, 13.33%/4.94%/0 and 7.84%/0/0 respectively. The QTL mapping results in BC1 population were consistent with those in F2 population, except fewer QTLs detected. Most QTLs identified were minor-effect ones, only a few were main-effect ones (PVE > 10%), focused on 2 traits, Wue and Tr, such as qWue1.1, qWue1.2, FqTr1.1, FqTr6, BqWue1.1 and BqTr3; The epistatic effects, especially those related to the dominance effects were the main genetic component of photosynthetic traits, and all the epistatic QTLs had no single-locus effects except qPn1.2, FqGs1.2, FqCi1.2 and qCc3.2; The detected QTLs underlying each trait varied at different stages except stable QTLs qGs1.1, detected at 3 stages, qWue2, qTr1.2 and qCc3.2, detected at 2 stages; 6 co-located QTLs were identified, each of which conferring 2–5 different traits, demonstrated the gene pleiotropy between photosynthetic traits; 2 QTL clusters, located within the marker intervals RCM1842-RCM1335 and RCM523-RCM83, contained 15/5 (F2/BC1) and 4/4 (F2/BC1) QTLs conferring multiple traits, including co-located QTLs and main-effect QTLs. The above results provided new insights into the genetic structure of photosynthetic traits and important references for the high photosynthetic efficiency breeding in castor plant.

Genetic Dissection of Extreme Seed-Flooding Tolerance in a Wild Soybean PI342618B by Linkage Mapping and Candidate Gene Analysis.

Seed-flooding stress is one of major abiotic constraints that adversely affects soybean production worldwide. Identifying tolerant germplasms and revealing the genetic basis of seed-flooding tolerance are imperative goals for soybean breeding. In the present study, high-density linkage maps of two inter-specific recombinant inbred line (RIL) populations, named NJIRNP and NJIR4P, were utilized to identify major quantitative trait loci (QTLs) for seed-flooding tolerance using three parameters viz., germination rate (GR), normal seedling rate (NSR), and electrical conductivity (EC). A total of 25 and 18 QTLs were detected by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 12 common QTLs were identified through both methods. All favorable alleles for the tolerance are notably from the wild soybean parent. Moreover, four digenic epistatic QTL pairs were identified, and three of them showed no main effects. In addition, the pigmented soybean genotypes exhibited high seed-flooding tolerance compared with yellow seed coat genotypes in both populations. Moreover, out of five identified QTLs, one major region containing multiple QTLs associated with all three traits was identified on Chromosome 8, and most of the QTLs within this hotspot were major loci (R2 > 10) and detectable in both populations and multiple environments. Based on the gene expression and functional annotation information, 10 candidate genes from QTL "hotspot 8-2" were screened for further analysis. Furthermore, the results of qRT-PCR and sequence analysis revealed that only one gene, GmDREB2 (Glyma.08G137600), was significantly induced under flooding stress and displayed a TTC tribasic insertion mutation of the nucleotide sequence in the tolerant wild parent (PI342618B). GmDREB2 encodes an ERF transcription factor, and the subcellular localization analysis using green fluorescent protein (GFP) revealed that GmDREB2 protein was localized in the nucleus and plasma membrane. Furthermore, overexpression of GmDREB2 significantly promoted the growth of soybean hairy roots, which might indicate its critical role in seed-flooding stress. Thus, GmDREB2 was considered as the most possible candidate gene for seed-flooding tolerance.

Genetic interrogation of phenotypic plasticity informs genome-enabled breeding in cotton

Phenotypic plasticity, or the ability to adapt to and thrive in changing climates and variable environments, is essential for developmental programs in plants. Despite its importance, the genetic underpinnings of phenotypic plasticity for key agronomic traits remain poorly understood in many crops. In this study, we aim to fill this gap by using genome-wide association studies to identify genetic variations associated with phenotypic plasticity in upland cotton (Gossypium hirsutum L.). We identified 73 additive quantitative trait loci (QTLs), 32 dominant QTLs, and 6799 epistatic QTLs associated with 20 traits. We also identified 117 additive QTLs, 28 dominant QTLs, and 4691 epistatic QTLs associated with phenotypic plasticity in 19 traits. Our findings reveal new genetic factors, including additive, dominant, and epistatic QTLs, that are linked to phenotypic plasticity and agronomic traits. Meanwhile, we find that the genetic factors controlling the mean phenotype and phenotypic plasticity are largely independent in upland cotton, indicating the potential for simultaneous improvement. Additionally, we envision a genomic design strategy by utilizing the identified QTLs to facilitate cotton breeding. Taken together, our study provides new insights into the genetic basis of phenotypic plasticity in cotton, which should be valuable for future breeding.

Efficiency of mapping epistatic quantitative trait loci.

Most theoretical studies on epistatic QTL mapping have shown that this procedure is powerful, efficient to control the false positive rate (FPR), and precise to localize QTLs. The objective of this simulation-based study was to show that mapping epistatic QTLs is not an almost-perfect process. We simulated 50 samples of 400 F2 plants/recombinant inbred lines, genotyped for 975 SNPs distributed in 10 chromosomes of 100 cM. The plants were phenotyped for grain yield, assuming 10 epistatic QTLs and 90 minor genes. Adopting basic procedures of r/qtl package, we maximized the power of detection for QTLs (56-74%, on average) but associated with a very high FPR (65%) and a low detection power for the epistatic pairs (7%). Increasing the average detection power for epistatic pairs (14%) highly increased the related FPR. Adopting a procedure to find the best balance between power and FPR, there was a significant decrease in the power of QTL detection (17-31%, on average), associated with a low average detection power for epistatic pairs (8%) and an average FPR of 31% for QTLs and 16% for epistatic pairs. The main reasons for these negative results are a simplified specification of the coefficients of epistatic effects, as theoretically proved, and the effects of minor genes since 2/3 of the FPR for QTLs were due to them. We hope that this study, including the partial derivation of the coefficients of epistatic effects, motivates investigations on how to increase the power of detection for epistatic pairs, effectively controlling the FPR.

Novel quantitative trait loci for yield and yield related traits identified in Basmati rice (Oryza sativa)

AbstractIncreasing crop productivity is one of the prime goals of crop breeding research. Rice grain yield is a complex quantitative trait governed by polygenes. Although several QTLs governing grain yield traits have been reported and limited attempts have been made to map QTLs for grain yield parameters in Basmati rice. A population from the cross Sonasal and Pusa Basmati 1121 comprising 352 RILs was generated through the single seed descent method. A total of 12 QTLs governing yield and yield‐related traits were mapped on six chromosomes, namely, 1, 2, 3, 7, 8 and 9, of which five QTLs were novel. We identified a novel and robust epistatic QTL (qPH1.1 and qPL1.1) governing plant height and panicle length, flanked by the markers RM5336‐RM1 on chromosome 1. The gene encoding brassinosteroid insensitive 1‐associated receptor kinase 1 precursor is the putative candidate gene underlying this epistatic QTL. Another novel QTL, qNT3.1, governing tiller number was bracketed to a region of .77 Mb between the markers RM15247 and RM15281 on chromosome 3. Of the 57 annotated gene models, Os03g0437600 encoding alpha/beta‐fold hydrolase, a homologous to AtKai2 is a putative candidate gene underlying the novel QTL qNT3.1. The other QTLs such as qDFF1.1 governing days to 50% flowering co‐localizes with the gene Ghd7, QTL for plant height qPH1.2 co‐localizes with the gene sd1, the QTLs for panicle length co‐localizes with FUWA and DEP2, the QTL for tiller number co‐localizes with OsRLCK57 and QTLs for thousand‐grain weight co‐localize with the major gene GS3. The QTLs identified in the current study can be effectively used in marker‐assisted selection for developing Basmati rice varieties with a higher yield.

Genetic dissection of additive and epistatic quantitative trait loci controlling pod number per plant in peanut (Arachis hypogaea L.)

Genetic dissection of additive and epistatic quantitative trait loci controlling pod number per plant in peanut (Arachis hypogaea L.)

QTL localisation of seed-related traits in Tibetan hulless barley based on a high-density single-nucleotide polymorphism genetic map

The effective use of high-quality and high-yielding germplasm resources is of great importance for the development of hulless barley varieties. Therefore, the search for such resources has long been a goal of the breeding community. In this study, a genotyping-by-sequencing (GBS) analysis was performed on an F2 (Nierumuzha × Kunlun 10) population. A high-density genetic map of hulless barley was constructed, which contained 1 475 efficient single-nucleotide polymorphism markers with 7 052 bin markers. The total length of the seven chromosomes was 1 139.4 cM, with an average marker distance of 0.59 cM. Based on this high-density linkage map, a total of 54 quantitative trait loci (QTLs) related to the seed traits were detected, including seed colour (SC), thousand kernel weight (TKW), seed average area (SAA), seed perimeter (SP), seed length (SL), seed width (SW), seed length/width (SLW), seed diameter (SD), and seed circular degree (SCD). These QTLs explained 3.32–35.73% (mean = 11.45%) of the phenotypic interpretation, including 24 main QTLs and 30 epistatic QTLs. A total of 24 candidate genes were identified within the QTL region, including one SC-associated transcription factor (ANT1), two TSW-related genes, a transcription factor (NAC021), a gene associated with the non-homologous end joining (NHEJ) pathway (ku70), three SAA-associated genes (LOL2, NAC021, TSK), two SL-associated genes (MADS21, MADS4), six SW-associated genes (FIP1, NAC021, DREB 1A, HVA22A, CYP78A6, SAUR71), five LW-related genes (NAM-B2, CRY1, LHY, CYP710A1, WRKY72), two SP-related genes (SKIP11, TCP18), two SD-related genes (NAC021, SKIP8), and three SCD-related genes (MYB1R1, RAX3, NAC100). These genes are involved in the regulation of the cell development, material transport, signal transduction, and plant morphogenesis and play an important role in the regulation of agronomic traits in hulless barley. The high-density genetic mapping and QTL identification of the seed traits in hulless barley provide a valuable genetic resource and the basis for further molecular marker-assisted selection and genomic studies.

QTL analysis for nitrogen use efficiency in wheat (Triticum aestivum L.)

The genetic architecture of nitrogen use efficiency (NUE) and its two component traits i.e. NUpE (N uptake efficiency) and NUtE (N utilization efficiency) was studied using a bi-parental RIL mapping population derived from a cross HUW468 (high NUE)/C306 (low NUE). The mapping population, two parental genotypes and three check genotypes were evaluated under four different N levels (0, 60, 120, and 180 kg/ha) over three years. A genetic map containing 456 SNP markers (2571.38 cM length) was used for QTL analysis. Thirty six main effect QTLs (17 QTLs for NUE, 13 NUpE and 6 QTLs for NUtE) distributed on 12 chromosomes (1B, 1D, 2A, 2B, 3A, 4B, 5A, 5B, 5D, 6A, 6D, and 7A) were identified at 2.52–9.27 LOD scores. Individual QTLs explained 6.65–22.89% phenotypic variation. Multi-traits QTLs (Mt-QTLs) and epistatic QTLs involving first-order epistatic (QTL × QTL) interactions were also discovered. Candidate genes (CGs, as many as 737) were mined from QTL regions which were mainly involved in metabolic process, cellular process and catalytic activity, etc.; differential expression was observed for 49 CGs in roots and 34 in shoots. The CGs encoded important transcription factors, transporters, etc. having a role in NUE. QTLs and CGs reported in this study enriched the available knowledge. Seven QTLs (including three Mt-QTLs) and QTLs involved in six epistatic interactions are recommended for MAS for improvement of NUE in wheat.

Genetic Analysis and Quantitative Trait Locus Mapping using the Major Gene Plus Polygene Model for Soybean [Glycine max (L.) Merr.] Main Quality Trait

Background: Soybean is an important oil and cash crop, soybean protein is the main source of protein for human consumption. Soybean oil is also major edible oil that is rich in unsaturated fatty acids and is more conducive to human long-term consumption than animal oil. An important research direction of current soybean breeding is improvement of the protein and fat content using marker assisted selection and many reports of associated quantitative trait locus (QTL) mapping have been documented. Until to now, a total of 241 QTLs associated with protein content and 315 related to fat content have been collected on the SoyBase website. Methods: In this study, 236 F2 generation plants and a derivative group were constructed by using Jiyu50 and Jinong18, obtained from Jilin Province. Combining three years of phenotypic and molecular detection data, using the ICIM method, one-dimensional scanning detected 24 QTLs related to protein and fat content, two-dimensional ICIM analysis of F2:3 families detected 7 pairs of epistatic QTLs associated with fat content. Result: Based on a mixed model for major genes and polygenes, the C model was determined the optimal genetic model for protein and fat content, with genetic rates of multiple genes of 71.15% and 79.15%, respectively. In the detection of high-oil molecular markers, the detection coincidence degree was Sat_238 greater than Satt 100 greater than Satt 150 greater than Satt 636 greater than Sat_287 = Sat_342, while this was Satt150 = Sat_342 greater than Satt100 greater than Satt 636 greater than Sat_287 greater than Sat_238 for high-protein markers. The QTL localization results showed that 9 microeffect QTLs related to protein content and 7 microeffect QTLs related to fat content were detected. Isolation analysis results were generally similar to those of QTL mapping. One stable QTL associated with protein content and two stable QTLs associated with fat content were identified as being of some application value in soybean molecular marker-assisted breeding.

Genetic dissection of grain traits and their corresponding heterosis in an elite hybrid.

Rice productivity has considerably improved due to the effective employment of heterosis, but the genetic basis of heterosis for grain shape and weight remains uncertain. For studying the genetic dissection of heterosis for grain shape/weight and their relationship with grain yield in rice, quantitative trait locus (QTL) mapping was performed on 1,061 recombinant inbred lines (RILs), which was developed by crossing xian/indica rice Quan9311B (Q9311B) and Wu-shan-si-miao (WSSM). Whereas, BC1F1 (a backcross F1) was developed by crossing RILs with Quan9311A (Q9311A) combined with phenotyping in Hefei (HF) and Nanning (NN) environments. Overall, 114 (main-effect, mQTL) and 359 (epistatic QTL, eQTL) were identified in all populations (RIL, BC1F1, and mid-parent heterosis, HMPs) for 1000-grain weight (TGW), grain yield per plant (GYP) and grain shape traits including grain length (GL), grain width (GW), and grain length to width ratio (GLWR). Differential QTL detection revealed that all additive loci in RILs population do not show heterotic effects, and few of them affect the performance of BC1F1. However, 25 mQTL not only contributed to BC1F1’s performance but also contributed to heterosis. A total of seven QTL regions was identified, which simultaneously affected multiple grain traits (grain yield, weight, shape) in the same environment, including five regions with opposite directions and two regions with same directions of favorable allele effects, indicating that partial genetic overlaps are existed between different grain traits. This study suggested different approaches for obtaining good grain quality with high yield by pyramiding or introgressing favorable alleles (FA) with the same direction of gene effect at the QTL regions affecting grain shape/weight and grain yield distributing on different chromosomes, or introgressing or pyramiding FA in the parents instead of fixing additive effects in hybrid as well as pyramiding the polymorphic overdominant/dominant loci between the parents and eliminating underdominant loci from the parents. These outcomes offer valuable information and strategy to develop hybrid rice with suitable grain type and weight.

WheatQTLdb V2.0: a supplement to the database for wheat QTL.

We recently developed a database for hexaploid wheat QTL (WheatQTLdb; www.wheatqtldb.net), which included 11,552 QTL affecting various traits of economic importance. However, that database did not include valuable QTL from other wheat species and/or progenitors of hexaploidwheat. Therefore, an updated and improved version of wheat QTL database (WheatQTLdb V2.0) was developed, which now includes information on hexaploid wheat (Triticum aestivum) and the following seven other related species: T. durum, T. turgidum, T. dicoccoides, T. dicoccum, T. monococcum, T. boeoticum, and Aegilops tauschii. WheatQTLdb V2.0 includes a much-improved list of QTL, including 27,518 main effect QTL, 202 epistatic QTL, and 1321 metaQTL. This newly released WheatQTLdb V2.0 also has additional valuable options to search and choose the QTL, category-wise, and trait-wise data for their use inresearch or breeding programs.

Identification of a stable major-effect quantitative trait locus for pre-harvest sprouting in common wheat (Triticum aestivum L.) via high-density SNP-based genotyping.

A major and stable QTL cQSGR.sau.3D, which can explain 33.25% of the phenotypic variation in SGR, was mapped and validated, and cQSGR.sau.3D was found to be independent of GI. In this study, a recombinant inbred line (RIL) population containing 304 lines derived from the cross of Chuan-nong17 (CN17) and Chuan-nong11 (CN11) was genotyped using the Wheat55K single-nucleotide polymorphism array. A high-density genetic map consisting of 8329 markers spanning 4131.54cM and distributed across 21 wheat chromosomes was constructed. QTLs for whole spike germination rate (SGR) were identified in multiple years. Six and fourteen QTLs were identified using the Inclusive Composite Interval Mapping-Biparental Populations and Multi-Environment Trial methods, respectively. A total of 106 digenic epistatic QTLs were also detected in this study. One of the additive QTLs, cQSGR.sau.3D, which was mapped in the region from 3.5 to 4.5cM from linkage group 3D-2 on chromosome 3D, can explain 33.25% of the phenotypic variation in SGR and be considered a major and stable QTL for SGR. This QTL was independent of the seeds' germination traits, such as germination index. One Kompetitive Allele-Specific PCR (KASP) marker, KASP-AX-110772653, which is tightly linked to cQSGR.sau.3D, was developed. The genetic effect of cQSGR.sau.3D on SGR in the RIL and natural populations was successfully confirmed. Furthermore, within the interval in which cQSGR.sau.3D is located in Chinese Spring reference genomes, thirty-seven genes were found. cQSGR.sau.3D may provide new resources for pre-harvest sprouting resistance breeding of wheat in the future.

Construction of a novel Wheat 55K SNP array-derived genetic map and its utilization in QTL mapping for grain yield and quality related traits.

Wheat is one of the most important staple crops for supplying nutrition and energy to people world. A new genetic map based on the Wheat 55 K SNP array was constructed using recombinant inbred lines derived from a cross between Zhongkemai138 and Kechengmai2 to explore the genetic foundation for wheat grain features. This new map covered 2,155.72 cM across the 21 wheat chromosomes with 11,455 markers. And 2,846 specific markers for this genetic map and 148 coincident markers among different maps were documented, which was helpful for improving and updating wheat genetic and genomic information. Using this map, a total of 68 additive QTLs and 82 pairs of epistatic QTLs were detected for grain features including yield, nutrient composition, and quality-related traits by QTLNetwork 2.1 and IciMapping 4.1 software. Fourteen additive QTLs and one pair of epistatic QTLs could be detected by both software programs and thus regarded as stable QTLs here, all of which explained higher phenotypic variance and thus could be utilized for wheat grain improvement. Additionally, thirteen additive QTLs were clustered into three genomic intervals (C4D.2, C5D, and C6D2), each of which had at least two stable QTLs. Among them, C4D.2 and C5D have been attributed to the famous dwarfing gene Rht2 and the hardness locus Pina, respectively, while endowed with main effects on eight grain yield/quality related traits and epistatically interacted with each other to control moisture content, indicating that the correlation of involved traits was supported by the pleotropic of individual genes but also regulated by the gene interaction networks. Additionally, the stable additive effect of C6D2 (QMc.cib-6D2 and QTw.cib-6D2) on moisture content was also highlighted, potentially affected by a novel locus, and validated by its flanking Kompetitive Allele-Specific PCR marker, and TraesCS6D02G109500, encoding aleurone layer morphogenesis protein, was deduced to be one of the candidate genes for this locus. This result observed at the QTL level the possible contribution of grain water content to the balances among yield, nutrients, and quality properties and reported a possible new locus controlling grain moisture content as well as its linked molecular marker for further grain feature improvement.