Quantitative trait loci for seed yield and yield-related traits, and their responses to reduced phosphorus supply in Brassica napus

Background and Aims

One of the key targets of breeding programmes in rapeseed (Brassica napus) is to develop high-yield varieties. However, the lack of available phosphorus (P) in soils seriously limits rapeseed production. The aim of this study was to dissect the genetic control of seed yield and yield-related traits in B. napus grown with contrasting P supplies.

Methods

Two-year field trials were conducted at one site with normal and low P treatments using a population of 124 recombinant inbred lines derived from a cross between ‘B104-2’ and ‘Eyou Changjia’. Seed yield, seed weight, seed number, pod number, plant height, branch number and P efficiency coefficient (PEC) were investigated. Quantitative trait locus (QTL) analysis was performed by composite interval mapping.

Key Results

The phenotypic values of most of the tested traits were reduced under the low P conditions. In total, 74 putative QTLs were identified, contributing 7·3–25·4 % of the phenotypic variation. Of these QTLs, 16 (21·6 %) were detected in two seasons and in the mean value of two seasons, and eight QTLs for two traits were conserved across P levels. Low-P-specific QTLs were clustered on chromosomes A1, A6 and A8. By comparative mapping between Arabidopsis and B. napus, 161 orthologues of 146 genes involved in Arabidopsis P homeostasis and/or yield-related trait control were associated with 45 QTLs corresponding to 23 chromosomal regions. Four gene-based markers developed from genes involved in Arabidopsis P homeostasis were mapped to QTL intervals.

Conclusions

Different genetic determinants were involved in controlling seed yield and yield-related traits in B. napus under normal and low P conditions. The QTLs detected under reduced P supply may provide useful information for improving the seed yield of B. napus in soils with low P availability in marker-assisted selection.     

Genomic distribution of quantitative trait loci for yield and yield-related traits in common wheat

A major objective of quantitative trait locus(QTL)studies is to find genes/markers that can be used in breeding programs via marker assisted selection(MAS).We surveyed the QTLs for yield and yield related traits and their genomic distributions in common wheat(Triticum aestivum L.)in the available published reports.We then carried out a meta-QTL(MQTL)analysis to identify the major and consistent QTLs for these traits.In total,55 MQTLs were identified,of which 12 significant MQTLs were located on wheat chromosomes 1A,1B,2A,2D,3B,4A,4B,4D and 5A.Our study showed that the genetic control of yield and its components in common wheat involved the important genes such as Rht and Vrn.Furthermore,several significant MQTLs were found in the chromosomal regions corresponding to several rice genomic locations containing important QTLs for yield related traits.Our results demonstrate that meta-QTL analysis is a powerful tool for confirming the major and stable QTLs and refining their chromosomal positions in common wheat,which may be useful for improving the MAS efficiency of yield related traits.     

Mapping of quantitative trait loci and development of allele-specific markers for seed weight in Brassica napus

Seed weight is an important component of grain yield in oilseed rape (Brassica napus L.), but the genetic basis for the important quantitative trait is still not clear. In order to identify the genes for seed weight in oilseed rape, QTL mapping for thousand seed weight (TSW) was conducted with a doubled haploid (DH) population and an F₂ population. A complete linkage map of the DH population was constructed using 297 simple sequence repeat (SSR) markers. Among nine TSW QTLs detected, two major QTLs, TSWA7a and TSWA7b, were stably identified across years and collectively explained 27.6–37.9% of the trait variation in the DH population. No significant epistatic interactions for TSW detected in the DH population indicate that the seed weight variation may be primarily attributed to additive effects. The stability and significance of TSWA7a and TSWA7b were further validated in the F₂ population with different genetic backgrounds. By cloning BnMINI3a and BnTTG2a, two B. napus homologous genes to Arabidopsis thaliana, allele-specific markers were developed for TSWA5b and TSWA5c, two TSW QTLs on A5, respectively. The importance of the major and minor QTLs identified was further demonstrated by analysis of the allelic effects on TSW in the DH population.     

In silico mapping of quantitative trait loci in maize

Quantitative trait loci (QTL) are most often detected through designed mapping experiments. An alternative approach is in silico mapping, whereby genes are detected using existing phenotypic and genomic databases. We explored the usefulness of in silico mapping via a mixed-model approach in maize (Zea mays L.). Specifically, our objective was to determine if the procedure gave results that were repeatable across populations. Multilocation data were obtained from the 1995–2002 hybrid testing program of Limagrain Genetics in Europe. Nine heterotic patterns comprised 22,774 single crosses. These single crosses were made from 1,266 inbreds that had data for 96 simple sequence repeat (SSR) markers. By a mixed-model approach, we estimated the general combining ability effects associated with marker alleles in each heterotic pattern. The numbers of marker loci with significant effects—37 for plant height, 24 for smut [Ustilago maydis (DC.) Cda.] resistance, and 44 for grain moisture—were consistent with previous results from designed mapping experiments. Each trait had many loci with small effects and few loci with large effects. For smut resistance, a marker in bin 8.05 on chromosome 8 had a significant effect in seven (out of a maximum of 18) instances. For this major QTL, the maximum effect of an allele substitution ranged from 5.4% to 41.9%, with an average of 22.0%. We conclude that in silico mapping via a mixed-model approach can detect associations that are repeatable across different populations. We speculate that in silico mapping will be more useful for gene discovery than for selection in plant breeding programs.     

RFLP mapping of quantitative trait loci controlling seed aliphatic-glucosinolate content in oilseed rape (Brassica napus L)

We report the RFLP mapping of quantitative trait loci (QTLs) which regulate the total seed aliphaticglucosinolate content in Brassica napus L. A population of 99 F₁-derived doubled-haploid (DH) recombinant lines from a cross between the cultivars Stellar (low-glucosinolate) and Major (high-glucosinolate) was used for singlemarker analysis and the interval mapping of QTLs associated with total seed glucosinolates. Two major loci, GSL-1 and GSL-2, with the largest influence on total seed aliphatic-glucosinolates, were mapped onto LG 20 and LG 1, respectively. Three loci with smaller effects, GSL-3, GSL-4 and GSL-5, were tentatively mapped to LG 18, LG 4 and LG 13, respectively. The QTLs acted in an additive manner and accounted for 71 % of the variation in total seed glucosinolates, with GSL-1 and GSL-2 accounting for 33% and 17%, respectively. The recombinant population had aliphatic-glucosinolate levels of between 6 and 160 moles per g^-1 dry wt of seed. Transgressive segregation for high seed glucosinolate content was apparent in 25 individuals. These phenotypes possessed Stellar alleles at GSL-3 and Major alleles at the four other GSL loci demonstrating that low-glucosinolate genotypes (i.e. Stellar) may possess alleles for high glucosinolates which are only expressed in particular genetic backgrounds. Gsl-elong and Gsl-alk, loci which regulate the ratio of individual aliphatic glucosinolates, were also mapped. Gsl-elong-1 and Gsl-elong-2, which control elongation of the -amino-acid precursors, mapped to LG 18 and LG 20 and were coincident with GSL loci which regulate total seed aliphatic glucosinolates. A third tentative QTL, which regulates side-chain elongation, was tentatively mapped to LG 12. Gsl-alk, which regulates H₃CS-removal and side-chain de-saturation, mapped to LG 20.     

Association mapping of seed oil content in Brassica napus and comparison with quantitative trait loci identified from linkage mapping

Association mapping has been used increasingly in natural populations with rich genetic diversity to detect DNA-based markers that are associated with important agronomic traits. Brassica napus is an important oil crop with limited genetic diversity. "New-type" B. napus that is introgressed with subgenomic components from related species has been developed to broaden the genetic basis of "traditional" B. napus. In this study, new-type B. napus lines and a collection of traditional B. napus varieties from different countries were used as two different populations to evaluate seed oil content and to determine the efficacy of association mapping by comparison with previous study of linkage mapping. Relatively rich genetic diversity, but a higher level of linkage disequilibrium was observed in the new-type B. napus as compared with the traditional B. napus. Similarly, a larger variation in oil content and a greater number of associated markers were detected in the population of new-type B. napus. Meanwhile, more than half of the genetic loci, to which the associated markers corresponded, were located within the quantitative trait loci intervals identified previously in linkage mapping experiments, which demonstrated the power of association mapping in B. napus.     

Syntenic quantitative trait loci and genomic divergence for Sclerotinia resistance and flowering time in Brassica napus

Oilseed rape (Brassica napus) is an allotetraploid with two subgenomes descended from a common ancestor. Accordingly, its genome contains syntenic regions with many duplicate genes, some of which may have retained their original functions, whereas others may have diverged. Here, we mapped quantitative trait loci (QTL) for stem rot resistance (SRR), a disease caused by the fungus Sclerotinia sclerotiorum, and flowering time (FT) in a recombinant inbred line population. The population was genotyped using B. napus 60K single nucleotide polymorphism arrays and phenotyped in six (FT) and nine (SSR) experimental conditions or environments. In total, we detected 30 SRR QTL and 22 FT QTL and show that some of the major QTL associated with these two traits were co-localized, suggesting a genetic linkage between them. Two SRR QTL on chromosome A2 and two on chromosome C2 were shown to be syntenic, suggesting the functional conservation of these regions. We used the syntenic properties of the genomic regions to exclude genes for selection candidates responsible for QTL-associated traits. For example, 152 of the 185 genes could be excluded from a syntenic A2-C2 region. These findings will help to elucidate polyploid genomics in future studies, in addition to providing useful information for B. napus breeding programs.     

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

Key message

A comprehensive linkage atlas for seed yield in rapeseed.

Abstract

Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.

     

Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits

The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F₂ (IF₂) to the two parents, from random-cross of IF₂ or from selfing of IF₂ population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF₂ population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.     

Quantitative trait loci affecting seed mineral concentrations in Brassica napus grown with contrasting phosphorus supplies

MethodsA population of 124 recombinant inbred lines derived from a cross between P-inefficient ‘B104-2’ and P-efficient ‘Eyou Changjia’ was used for phenotypic investigation and QTL analysis. Two-year field trials were conducted with two P treatments. Concentrations of mineral elements (P, Ca, Mg, Fe, Zn, Cu and Mn) in seeds were determined and QTLs were identified by composite interval mapping.ConclusionsThe accumulation of mineral elements in seeds is controlled by multiple genes. Common physiological and molecular mechanisms could be involved in the accumulation of several mineral elements, and genes involved in these processes in B. napus are suggested. These results offer insights to the genetic basis of seed mineral accumulation across different P levels in B. napus.     

Bayesian quantitative trait loci mapping for multiple traits

Most quantitative trait loci (QTL) mapping experiments typically collect phenotypic data on multiple correlated complex traits. However, there is a lack of a comprehensive genomewide mapping strategy for correlated traits in the literature. We develop Bayesian multiple-QTL mapping methods for correlated continuous traits using two multivariate models: one that assumes the same genetic model for all traits, the traditional multivariate model, and the other known as the seemingly unrelated regression (SUR) model that allows different genetic models for different traits. We develop computationally efficient Markov chain Monte Carlo (MCMC) algorithms for performing joint analysis. We conduct extensive simulation studies to assess the performance of the proposed methods and to compare with the conventional single-trait model. Our methods have been implemented in the freely available package R/qtlbim (http://www.qtlbim.org), which greatly facilitates the general usage of the Bayesian methodology for unraveling the genetic architecture of complex traits.     

Association mapping of six yield-related traits in rapeseed (Brassica napus L.)

Yield is one of the most important traits for rapeseed (Brassica napus L.) breeding, but its genetic basis remains largely ambiguous. Association mapping has provided a robust approach to understand the genetic basis of complex agronomic traits in crops. In this study, a panel of 192 inbred lines of B. napus from all over the world was genotyped using 451 single-locus microsatellite markers and 740 amplified fragment length polymorphism markers. Six yield-related traits of these inbred lines were investigated in three consecutive years with three replications, and genome-wide association studies were conducted for these six traits. Using the model controlling both population structure and relative kinship (Q + K), a total of 43 associations (P < 0.001) were detected using the means of the six yield-related traits across 3 years, with two to fourteen markers associated with individual traits. Among these, 18 markers were repeatedly detected in at least 2 years, and 12 markers were located within or close to QTLs identified in previous studies. Six markers commonly associated with correlated traits. Conditional association analysis indicated that five of the associations between markers and correlated traits are caused by one QTL with pleiotropic effects, and the remaining association is caused by linked but independent QTLs. The combination of favorable alleles of multiple associated markers significantly enhances trait performance, illustrating a great potential of utilization of the associations in rapeseed breeding programs.     

Mapping quantitative trait loci for traits defined as ratios

Many traits are defined as ratios of two quantitative traits. Methods of QTL mapping for regular quantitative traits are not optimal when applied to ratios due to lack of normality for traits defined as ratios. We develop a new method of QTL mapping for traits defined as ratios. The new method uses a special linear combination of the two component traits, and thus takes advantage of the normal property of the new variable. Simulation study shows that the new method can substantially increase the statistical power of QTL detection relative to the method which treats ratios as regular quantitative traits. The new method also outperforms the method that uses Box-Cox transformed ratio as the phenotype. A real example of QTL mapping for relative growth rate in soybean demonstrates that the new method can detect more QTL than existing methods of QTL mapping for traits defined as ratios.     

Multiple-interval mapping for quantitative trait loci controlling endosperm traits

Endosperm traits are trisomic inheritant and are of great economic importance because they are usually directly related to grain quality. Mapping for quantitative trait loci (QTL) underlying endosperm traits can provide an efficient way to genetically improve grain quality. As the traditional QTL mapping methods (diploid methods) are usually designed for traits under diploid control, they are not the ideal approaches to map endosperm traits because they ignore the triploid nature of endosperm. In this article, a statistical method considering the triploid nature of endosperm (triploid method) is developed on the basis of multiple-interval mapping (MIM) to map for the underlying QTL. The proposed triploid MIM method is derived to broadly use the marker information either from only the maternal plants or from both the maternal plants and their embryos in the backcross and F2 populations for mapping endosperm traits. Due to the use of multiple intervals simultaneously to take multiple QTL into account, the triploid MIM method can provide better detection power and estimation precision, and as shown in this article it is capable of analyzing and searching for epistatic QTL directly as compared to the traditional diploid methods and current triploid methods using only one (or two) interval(s). Several important issues in endosperm trait mapping, such as the relation and differences between the diploid and triploid methods, variance components of genetic variation, and the problems if effects are present and ignored, are also addressed. Simulations are performed to further explore these issues, to investigate the relative efficiency of different experimental designs, and to evaluate the performance of the proposed and current methods in mapping endosperm traits. The MIM-based triploid method can provide a powerful tool to estimate the genetic architecture of endosperm traits and to assist the marker-assisted selection for the improvement of grain quality in crop science. The triploid MIM FORTRAN program for mapping endosperm traits is available on the worldwide web (http://www.stat.sinica.edu.tw/chkao/).     

甘蓝型油菜遗传图谱的构建及单株产量构成因素的QTL分析

采用常规品系04-1139与高产多角果品系05-1054构建的F2代群体为作图群体, 运用SSR(Simple sequence repeat)和SRAP(Sequence-related amplified polymorphism)构建分子标记遗传图谱并对甘蓝型油菜单株产量构成因素进行QTL分析。遗传图谱包含200个分子标记, 分布于19个连锁群上, 总长度1 700.23 cM, 标记间的平均距离8.50 cM。采用复合区间作图法(Composite interval mapping, CIM)对单株产量构成因素(单株有效角果数、每果粒数和千粒重)进行QTL分析, 共检测到12个QTL: 其中单株有效角果数4个QTL, 分别解释表型变异为35.64%、12.96%、28.71%和34.02%; 每果粒数获得5个QTL, 分别解释表型变异为8.41%、7.87%、24.37%、8.57%和14.31%; 千粒重获得３个QTL, 分别解释表型变异为2.33%、1.81%和1.86%。结果表明: 同一性状的等位基因增效作用可以同时来自高值亲本和低值亲本; 文章中与主效QTL连锁的标记可用于油菜产量性状的分子标记辅助选择和聚合育种。     

Identification of quantitative trait loci (QTL) for oil and protein contents and their relationships with other seed quality traits in Brassica juncea

A detailed RFLP-genomic map was used to study the genetics of oil, seed and meal protein and sum of oil and seed/meal protein contents in a recombinant doubled-haploid population developed by crossing black- and yellow-seeded Brassica juncea lines. Two yellow seed color genes (SC-B4, SC-A6) and one QTL for erucic acid content (E_1b) showed pleiotropic effect for oil, protein and sum of oil and seed/meal protein contents. Six (O-A1, O-A6, O-A9, O-B3, O-B4, O-B5) and five (SP-A1, SP-A9, SP-B4, SP-B6, SP-C) QTLs were significant for oil and seed protein contents, respectively. Tight linkage of three of these QTLs (SP-A1, SP-A9, SP-B4, O-A1, O-A9, O-B4), with opposite effects, poses challenge to the plant breeders for simultaneous improvement of negatively correlated (r = −0.7**) oil and seed protein contents. However, one QTL for oil content (O-B3) and two for seed protein content (SP-B6, SP-C) were found to be unlinked, which offer the possibility for simultaneous improvement of these two traits. QTLs significant for meal protein (MP-A1, MP-A6, MP-A9, MP-B5, MP-B6) were significant at least for oil, seed protein or sum of oil and seed/meal protein contents (T-A6, T-A7, T-B4, T-B5). Sum of oil and seed protein contents and sum of oil and meal protein contents had a perfect correlation, as well as same epistatic interactions and QTLs with similar additive effect. This indicates that protein in seed or meal has practically the same meaning for breeding purposes. Epistatic interactions were significant for the quality traits, and their linkage reflected association among the traits.     

Mapping quantitative trait loci for longitudinal traits in line crosses

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.     

Bayesian mapping of quantitative trait loci for complex binary traits

A complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request.     

Detection of quantitative trait loci for meat quality traits in cattle

A whole-genome scan was carried out to detect quantitative trait loci (QTL) affecting sensory, organoleptic, physical and chemical properties of meat. The study used phenotypic data from 235 second-generation cross-bred bull calves of a Charolais × Holstein experimental population. Loin muscle samples were evaluated for yield force, intramuscular fat and nitrogen contents, myofibrillar fragmentation index, haem pigment concentration, moisture content and pH at 24 h postmortem. A sensory assessment was performed on grilled loin and roasted silverside joints by trained panellists. A linear regression analysis based on 165 markers revealed 35 QTL at the 5% chromosome-wide significance level (20 for sensory traits and 15 for physical and chemical traits), five of which were highly significant ( F -value: ≥9). The most significant QTL was located on chromosome 6 (with the best likely position at 39 cM) and affected haem pigment concentration. The Holstein allele for this QTL was associated with an increase of 0.53 SD in the haem scores. A QTL for pH_24h was identified on chromosome 14 (at 40 cM) and a QTL for moisture content was identified on chromosome 22 (at 21 cM). Two highly significant QTL were identified for sensory panel-assessed traits: beef odour intensity (grilled sample) on chromosome 10 (at 119 cM), and juiciness (roast sample) on chromosome 16 (at 70 cM). The proportion of phenotypic variance explained by the significant QTL ranged from 3.6% (for nitrogen content on chromosome 10) to 9.5% (for juiciness, roast sample on chromosome 16).