Evaluation of multi-locus models for genome-wide association studies: a case study in sugar beet

Association mapping has become a widely applied genomic approach to dissect the genetic architecture of complex traits. A major issue for association mapping is the need to control for the confounding effects of population structure, which is commonly done by mixed models incorporating kinship information. In this case study, we employed experimental data from a large sugar beet population to evaluate multi-locus models for association mapping. As in linkage mapping, markers are selected as cofactors to control for population structure and genetic background variation. We compared different biometric models with regard to important quantitative trait locus (QTL) mapping parameters like the false-positive rate, the QTL detection power and the predictive power for the proportion of explained genotypic variance. Employing different approaches we show that the multi-locus model, that is, incorporating cofactors, outperforms the other models, including the mixed model used as a reference model. Thus, multi-locus models are an attractive alternative for association mapping to efficiently detect QTL for knowledge-based breeding.     

Dissecting the genetic architecture of agronomic traits in multiple segregating populations in rapeseed (<Emphasis Type="Italic">Brassica napus</Emphasis> L.)

Detection of QTL in multiple segregating populations is of high interest as it includes more alleles than mapping in a single biparental population. In addition, such populations are routinely generated in applied plant breeding programs and can thus be used to identify QTL which are of direct relevance for a marker-assisted improvement of elite germplasm. Multiple-line cross QTL mapping and joint linkage association mapping were used for QTL detection. We empirically compared these two different biometrical approaches with regard to QTL detection for important agronomic traits in nine segregating populations of elite rapeseed lines. The plants were intensively phenotyped in multi-location field trials and genotyped with 253 SNP markers. Both approaches detected several additive QTL for diverse traits, including flowering time, plant height, protein content, oil content, glucosinolate content, and grain yield. In addition, we identified one epistatic QTL for flowering time. Consequently, both approaches appear suited for QTL detection in multiple segregating populations.     

Association mapping in multiple segregating populations of sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.)

Association mapping in multiple segregating populations (AMMSP) combines high power to detect QTL in genome-wide approaches of linkage mapping with high mapping resolution of association mapping. The main objectives of this study were to (1) examine the applicability of AMMSP in a plant breeding context based on segregating populations of various size of sugar beet (Beta vulgaris L.), (2) compare different biometric approaches for AMMSP, and (3) detect markers with significant main effect across locations for nine traits in sugar beet. We used 768 F _n (n = 2, 3, 4) sugar beet genotypes which were randomly derived from 19 crosses among diploid elite sugar beet clones. For all nine traits, the genotypic and genotype × location interaction variances were highly significant (P < 0.01). Using a one-step AMMSP approach, the total number of significant (P < 0.05) marker-phenotype associations was 44. The identification of genome regions associated with the traits under consideration indicated that not only segregating populations derived from crosses of parental genotypes in a systematic manner could be used for AMMSP but also populations routinely derived in plant breeding programs from multiple, related crosses. Furthermore, our results suggest that data sets, whose size does not permit analysis by the one-step AMMSP approach, might be analyzed using the two-step approach based on adjusted entry means for each location without losing too much power for detection of marker-phenotype associations.     

Mapping of epistatic quantitative trait loci in four-way crosses

Four-way crosses (4WC) involving four different inbred lines often appear in plant and animal commercial breeding programs. Direct mapping of quantitative trait loci (QTL) in these commercial populations is both economical and practical. However, the existing statistical methods for mapping QTL in a 4WC population are built on the single-QTL genetic model. This simple genetic model fails to take into account QTL interactions, which play an important role in the genetic architecture of complex traits. In this paper, therefore, we attempted to develop a statistical method to detect epistatic QTL in 4WC population. Conditional probabilities of QTL genotypes, computed by the multi-point single locus method, were used to sample the genotypes of all putative QTL in the entire genome. The sampled genotypes were used to construct the design matrix for QTL effects. All QTL effects, including main and epistatic effects, were simultaneously estimated by the penalized maximum likelihood method. The proposed method was confirmed by a series of Monte Carlo simulation studies and real data analysis of cotton. The new method will provide novel tools for the genetic dissection of complex traits, construction of QTL networks, and analysis of heterosis.     

Cross-validation in association mapping and its relevance for the estimation of QTL parameters of complex traits

Association mapping has become a widely applied genomic approach to identify quantitative trait loci (QTL) and dissect the genetic architecture of complex traits. However, approaches to assess the quality of the obtained QTL results are lacking. We therefore evaluated the potential of cross-validation in association mapping based on a large sugar beet data set. Our results show that the proportion of the population that should be used as estimation and validation sets, respectively, depends on the size of the mapping population. Generally, a fivefold cross-validation, that is, 20% of the lines as independent validation set, appears appropriate for commonly used population sizes. The predictive power for the proportion of genotypic variance explained by QTL was overestimated by on average 38% indicating a strong bias in the estimated QTL effects. The cross-validated predictive power ranged between 4 and 50%, which are more realistic estimates of this parameter for complex traits. In addition, QTL frequency distributions can be used to assess the precision of QTL position estimates and the robustness of the detected QTL. In summary, cross-validation can be a valuable tool to assess the quality of QTL parameters in association mapping.     

Conditional QTL mapping of oil content in rapeseed with respect to protein content and traits related to plant development and grain yield

Oil content in rapeseed (Brassica napus L.) is generally regarded as a character with high heritability that is negatively correlated with protein content and influenced by plant developmental and yield related traits. To evaluate possible genetic interrelationships between these traits and oil content, QTL for oil content were mapped using data on oil content and on oil content conditioned on the putatively interrelated traits. Phenotypic data were evaluated in a segregating doubled haploid population of 282 lines derived from the F₁ of a cross between the old German cultivar Sollux and the Chinese cultivar Gaoyou. The material was tested at four locations, two each in Germany and in China. QTLMapper version 1.0 was used for mapping unconditional and conditional QTL with additive (a) and locus pairs with additive × additive epistatic (aa) effects. Clear evidence was found for a strong genetic relationship between oil and protein content. Six QTL and nine epistatic locus pairs were found, which had pleiotropic effects on both traits. Nevertheless, two QTL were also identified, which control oil content independent from protein content and which could be used in practical breeding programs to increase oil content without affecting seed protein content. In addition, six additional QTL with small effects were only identified in the conditional mapping. Some evidence was apparent for a genetic interrelationship between oil content and the number of seeds per silique but no evidence was found for a genetic relationship between oil content and flowering time, grain filling period or single seed weight. The results indicate that for closely correlated traits conditional QTL mapping can be used to dissect the genetic interrelationship between two traits at the level of individual QTL. Furthermore, conditional QTL mapping can reveal additional QTL with small effects that are undetectable in unconditional mapping.     

Genetic basis of agronomically important traits in sugar beet (Beta vulgaris L.) investigated with joint linkage association mapping

Epistatic interactions may contribute substantially to the hybrid performance of sugar beet. The main goal of our study was to dissect the genetic basis of eight important physiological and agronomic traits using two different biometrical models for joint linkage association mapping. A total of 197 genotypes of an elite breeding population were evaluated in multi-location trials and fingerprinted with 194 SNP markers. Two different statistical models were used for the genome-wide scan for marker–trait associations: Model A, which corrects for the genetic background with markers as cofactors and Model B, which additionally models a population effect. Based on the extent of linkage disequilibrium in the parental population, we estimated that for a genome-wide scan at least 100 equally spaced markers are necessary. We mapped across the eight traits 39 QTL for Model A and 22 for Model B. Only 11% of the total number of QTL were identified based on Models A and B, which indicates that both models are complementary. Epistasis was detected only for two out of the eight traits, and contributed only to a minor extent to the genotypic variance. This low relevance of epistasis implies that in sugar beet breeding the prediction of performance of three-way hybrids is feasible with high accuracy based on the means of their single crosses.     

Application of a new IBD-based QTL mapping method to common wheat breeding population: analysis of kernel hardness and dough strength

Mapping quantitative trait loci (QTL) in plants is usually conducted using a population derived from a cross between two inbred lines. The power of such QTL detection and the estimation of the effects highly depend on the choice of the two parental lines. Thus, the QTL found represent only a small part of the genetic architecture and can be of limited economical interest in marker-assisted selection. On the other hand, applied breeding programmes evaluate large numbers of progeny derived from multiple-related crosses for a wide range of agronomic traits. It is assumed that the development of statistical techniques to deal with pedigrees in existing plant populations would increase the relevance and cost effectiveness of QTL mapping in a breeding context. In this study, we applied a two-step IBD-based-variance component method to a real wheat breeding population, composed of 374 F₆ lines derived from 80 different parents. Two bread wheat quality related traits were analysed by the method. Results obtained show very close agreement with major genes and QTL already known for those two traits. With this new QTL mapping strategy, inferences about QTL can be drawn across the breeding programme rather than being limited to the sample of progeny from a single cross and thus the use of the detected QTL in assisting breeding would be facilitated.     

Development of a near-isogenic line population of Arabidopsis thaliana and comparison of mapping power with a recombinant inbred line population

In Arabidopsis recombinant inbred line (RIL) populations are widely used for quantitative trait locus (QTL) analyses. However, mapping analyses with this type of population can be limited because of the masking effects of major QTL and epistatic interactions of multiple QTL. An alternative type of immortal experimental population commonly used in plant species are sets of introgression lines. Here we introduce the development of a genomewide coverage near-isogenic line (NIL) population of Arabidopsis thaliana, by introgressing genomic regions from the Cape Verde Islands (Cvi) accession into the Landsberg erecta (Ler) genetic background. We have empirically compared the QTL mapping power of this new population with an already existing RIL population derived from the same parents. For that, we analyzed and mapped QTL affecting six developmental traits with different heritability. Overall, in the NIL population smaller-effect QTL than in the RIL population could be detected although the localization resolution was lower. Furthermore, we estimated the effect of population size and of the number of replicates on the detection power of QTL affecting the developmental traits. In general, population size is more important than the number of replicates to increase the mapping power of RILs, whereas for NILs several replicates are absolutely required. These analyses are expected to facilitate experimental design for QTL mapping using these two common types of segregating populations.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

The Statistical Power of Inclusive Composite Interval Mapping in Detecting Digenic Epistasis Showing Common F2 Segregation Ratios

Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits,which could maintain additive variance and therefore assure the long-term genetic gain in breeding.Inclusive composite interval mapping（ICIM） is able to identify epistatic quantitative trait loci（QTLs） no matter whether the two interacting QTLs have any additive effects.In this article,we conducted a simulation study to evaluate detection power and false discovery rate（FDR） of ICIM epistatic mapping,by considering F₂ and doubled haploid（DH） populations,different F₂ segregation ratios and population sizes.Results indicated that estimations of QTL locations and effects were unbiased,and the detection power of epistatic mapping was largely affected by population size,heritability of epistasis,and the amount and distribution of genetic effects.When the same likelihood of odd（LOD） threshold was used,detection power of QTL was higher in F₂ population than power in DH population;meanwhile FDR in F₂ was also higher than that in DH.The increase of marker density from 10 cM to 5 cM led to similar detection power but higher FDR.In simulated populations,ICIM achieved better mapping results than multiple interval mapping（MIM） in estimation of QTL positions and effect.At the end,we gave epistatic mapping results of ICIM in one actual population in rice（Oryza sativa L.）.     

Genome-Wide Association Mapping for Yield and Other Agronomic Traits in an Elite Breeding Population of Tropical Rice (Oryza sativa)

Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations. In this study we performed association mapping for 19 agronomic traits including yield and yield components in a breeding population of elite irrigated tropical rice breeding lines so that the results would be more directly applicable to breeding than those from a diversity panel. The population was genotyped with 71,710 SNPs using genotyping-by-sequencing (GBS), and GWAS performed with the explicit goal of expediting selection in the breeding program. Using this breeding panel we identified 52 QTL for 11 agronomic traits, including large effect QTLs for flowering time and grain length/grain width/grain-length-breadth ratio. We also identified haplotypes that can be used to select plants in our population for short stature (plant height), early flowering time, and high yield, and thus demonstrate the utility of association mapping in breeding populations for informing breeding decisions. We conclude by exploring how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models.     

Identification of quantitative trait loci and epistatic interactions for plant height and heading date in rice

Appropriate heading date and plant height are prerequisites for attaining the desired yield level in rice breeding programs. In this study, we analyzed the genetic bases of heading date and plant height at both single- locus and two-locus levels, using a population of 240 F_2:3 families derived from a cross between two elite rice lines. Measurements for the traits were obtained over 2 years in replicated field trials. A linkage map was constructed with 151 polymorphic marker loci, based on which interval mapping was performed using Mapmaker/QTL. The analyses detected six QTLs for plant height and six QTLs for heading date; collectively the QTLs for heading date accounted for a much greater amount of phenotypic variation than did the QTLs for plant height. Two-way analyses of variance, with all possible two-locus combinations, detected large numbers (from 101 to 257) of significant digenic interactions in the 2 years for both traits involving markers distributed in the entire genome; 22 and 39 were simultaneously detected in both years for plant height and heading date, respectively. Each of the interactions individually accounted for only a very small portion of the phenotypic variation. The majority of the significant interactions involved marker loci that did not detect significant effects by single-locus analyses, and many of the QTLs detected by single-locus analyses were involved in epistatic interactions. The results clearly demonstrated the importance of epistatic interactions in the genetic bases of heading date and plant height. Received: 5 May 2001 / Accepted: 3 August 2001     

大白菜部分形态性状的QTL定位与分析

应用352个标记位点的大白菜AFLP和RAPD图谱和一套栽培品种间杂交获得的重组自交系群体,采用复合区间作图的方法对大白菜9个形态性状进行QTL定位及遗传效应研究。在14个连锁群上检测到50个QTL:其中控制株型的QTL有5个;控制株高的QTL有6个;控制开展度的QTL有5个;控制最大叶长的QTL有7个;控制最大叶宽的QTL有4个;控制叶形指数的QTL有6个;控制中肋长的QTL有7个;控制中肋宽的QTL有4个;控制抽苔的QTL有6个。另外,估算了单个QTL的遗传贡献率和加性效应。这将为大白菜品种改良中形态性状的分子标记辅助选择提供理论依据。     

Genome-based prediction of test cross performance in two subsequent breeding cycles

Genome-based prediction of genetic values is expected to overcome shortcomings that limit the application of QTL mapping and marker-assisted selection in plant breeding. Our goal was to study the genome-based prediction of test cross performance with genetic effects that were estimated using genotypes from the preceding breeding cycle. In particular, our objectives were to employ a ridge regression approach that approximates best linear unbiased prediction of genetic effects, compare cross validation with validation using genetic material of the subsequent breeding cycle, and investigate the prospects of genome-based prediction in sugar beet breeding. We focused on the traits sugar content and standard molasses loss (ML) and used a set of 310 sugar beet lines to estimate genetic effects at 384 SNP markers. In cross validation, correlations >0.8 between observed and predicted test cross performance were observed for both traits. However, in validation with 56 lines from the next breeding cycle, a correlation of 0.8 could only be observed for sugar content, for standard ML the correlation reduced to 0.4. We found that ridge regression based on preliminary estimates of the heritability provided a very good approximation of best linear unbiased prediction and was not accompanied with a loss in prediction accuracy. We conclude that prediction accuracy assessed with cross validation within one cycle of a breeding program can not be used as an indicator for the accuracy of predicting lines of the next cycle. Prediction of lines of the next cycle seems promising for traits with high heritabilities.     

Population-based resequencing analysis of improved wheat germplasm at wheat leaf rust resistance locus Lr21

Little is known about the genetic control of heterosis in the complex polyploid crop species oilseed rape (Brassica napus L.). In this study, two large doubled-haploid (DH) mapping populations and two corresponding sets of backcrossed test hybrids (THs) were analysed in controlled greenhouse experiments and extensive field trials for seedling biomass and yield performance traits, respectively. Genetic maps from the two populations, aligned with the help of common simple sequence repeat markers, were used to localise and compare quantitative trait loci (QTL) related to the expression of heterosis for seedling developmental traits, plant height at flowering, thousand seed mass, seeds per silique, siliques per unit area and seed yield. QTL were mapped using data from the respective DH populations, their corresponding TH populations and from mid-parent heterosis (MPH) data, allowing additive and dominance effects along with digenic epistatic interactions to be estimated. A number of genome regions containing numerous heterosis-related QTL involved in different traits and at different developmental stages were identified at corresponding map positions in the two populations. The co-localisation of per se QTL from the DH population datasets with heterosis-related QTL from the MPH data could indicate regulatory loci that may also contribute to fixed heterosis in the highly duplicated B. napus genome. Given the key role of epistatic interactions in the expression of heterosis in oilseed rape, these QTL hotspots might harbour genes involved in regulation of heterosis (including fixed heterosis) for different traits throughout the plant life cycle, including a significant overall influence on heterosis for seed yield.     

Multiple-line cross quantitative trait locus mapping in sugar beet (Beta vulgaris L.)

Linkage mapping based on multiple-line crosses is a promising strategy for mapping quantitative trait loci (QTL) underlying important agronomic traits. The main goal of this survey was to study the advantages of QTL mapping across versus within biparental populations using experimental data from three connected sugar beet (Beta vulgaris L.) populations evaluated for beet yield and potassium and sodium content. For the combined analysis across populations, we used two approaches for cofactor selection. In Model A, we assumed identical cofactors for every segregating population. In contrast, in Model B we selected cofactors specific for every segregating population. Model A performed better than Model B with respect to the number of QTL detected and the total proportion of phenotypic variance explained. The QTL analyses across populations revealed a substantially higher number of QTL compared to the analyses of single biparental populations. This clearly emphasizes the potential to increase QTL detection power with a joint analysis across biparental populations.     

Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F₂ populations of 150 F_2:3 families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R ² increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.     

大豆小区产量及其相关性状QTL间的上位性和环境互作效应

以晋豆23栽培大豆(Glycine max)为母本、灰布支黑豆(ZDD2315,半野生大豆)为父本衍生出447个RIL群体,通过构建SSR遗传图谱及利用混合线性模型分析方法,对2年大豆小区产量及主要植物学性状进行QTL定位,并作加性效应、加性×加性上位互作效应及环境互作效应分析。结果显示,共检测到12个与小区产量、单株粒重、单株茎重、单株粒茎比、有效分枝、主茎节数、株高和结荚高度相关的QTL,分别位于A1、A2、H_1、I、J_2和M连锁群上。其中小区产量、株高、单株粒重、有效分枝和主茎节数均表现为遗传正效应,即增加其性状的等位基因来源于母本晋豆23。同时,检测到11对影响小区产量、单株粒重、单株茎重、株高和结荚高度的加性×加性上位互作效应及环境互作效应的QTL,发现22个QTL与环境存在互作。实验结果表明,上位效应和QE互作效应对大豆小区产量及主要农艺性状的遗传影响很大。进行大豆分子标记辅助育种时,既要考虑效应起主要作用的QTL,又要注重上位性QTL,这样有利于性状的稳定表达和遗传。     

水稻株高构成因素的QTL剖析

利用水稻籼粳杂交 (圭 6 30× 0 2 42 8) F1 的花药离体培养建立的一个含 81个 DH家系的作图群体 ,对水稻株高构成因素 (穗长、第 1节间长、……、第 5节间长 )进行基因定位。DH群体中株高构成因素均呈正态分布。相邻的构成因素间呈极显著的正相关 ,而相距较远的构成因素间的相关较弱或不显著。采用 QTL(Quantitative trait lo-cus)分析 ,定位了影响株高构成因素的 6个 QTL:qtl7同时影响穗长和第 1、2、3节间长 ,qtl1 和 qtl2 同时影响第 4和第 5节间长 ,qtl1 0 a和 qtl1 0 b仅影响第 1节间长 ,qtl3 仅影响第 3节间长。采用 QTL 互作分析 ,检测到 19对显著的互作 ,每个构成因素受 2个或 2个以上的 QTL 互作对的影响。并且还发现 ,同一个 QTL 互作对可能影响不同的性状 ,以及一个 QTL 可以分别与不同的 QTL 产生互作而影响同一个性状或影响不同的性状 ,但总的看来 ,加性效应是主要的。这些结果揭示了株高构成因素间相关的遗传基础 ,在水稻育种中运用这些 QTL 将有助于对株高 ,以及对穗长和上部节间长度进行精细的遗传调控。