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.     

Highly efficient genotyping of rice biparental populations by GoldenGate assays based on parental resequencing

Key message

A new time- and cost-effective strategy was developed for medium-density SNP genotyping of rice biparental populations, using GoldenGate assays based on parental resequencing.

Abstract

Since the advent of molecular markers, crop researchers and breeders have dedicated huge amounts of effort to detecting quantitative trait loci (QTL) in biparental populations for genetic analysis and marker-assisted selection (MAS). In this study, we developed a new time- and cost-effective strategy for genotyping a population of progeny from a rice cross using medium-density single nucleotide polymorphisms (SNPs). Using this strategy, 728,362 “high quality” SNPs were identified by resequencing Teqing and Lemont, the parents of the population. We selected 384 informative SNPs that were evenly distributed across the genome for genotyping the biparental population using the Illumina GoldenGate assay. 335 (87.2 %) validated SNPs were used for further genetic analyses. After removing segregation distortion markers, 321 SNPs were used for linkage map construction and QTL mapping. This strategy generated SNP markers distributed more evenly across the genome than previous SSR assays. Taking the GW5 gene that controls grain shape as an example, our strategy provided higher accuracy (0.8 Mb) and significance (LOD 5.5 and 10.1) in QTL mapping than SSR analysis. Our study thus provides a rapid and efficient strategy for genetic studies and QTL mapping using SNP genotyping assays.     

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 an elite maize breeding population

Association mapping (AM) is a powerful approach to dissect the genetic architecture of quantitative traits. The main goal of our study was to empirically compare several statistical methods of AM using data of an elite maize breeding program with respect to QTL detection power and possibility to correct for population stratification. These models were based on the inclusion of cofactors (Model A), cofactors and population effect (Model B), and SNP effects nested within populations (Model C). A total of 930 testcross progenies of an elite maize breeding population were field-evaluated for grain yield and grain moisture in multi-location trials and fingerprinted with 425 SNP markers. For grain yield, population stratification was effectively controlled by Model A. For grain moisture with a high ratio of variance among versus within populations, Model B should be applied in order to avoid potential false positives. Model C revealed large differences among allele substitution effects for trait-associated SNPs across multiple plant breeding populations. This heterogeneous SNP allele substitution effects have a severe impact for genomic selection studies, where SNP effects are often assumed to be independent of the genetic background.     

Association mapping for phenological, morphological, and quality traits in canola quality winter rapeseed (Brassica napus L.)

QTL mapping by association analysis has recently gained interest in plant breeding research as an alternative to QTL mapping in segregating populations from biparental crosses. In a first experiment on whole-genome association analysis in rapeseed, 684 mapped AFLP markers were tested for association with 14 traits in a set of 84 canola quality winter rapeseed cultivars. For association analysis a general linear model was used. By testing significance of marker-trait associations against a false discovery rate of 0.2, between 1 and 34 associated markers were found for 10 of the 14 traits. Taking into account linkage disequilibrium between the significant markers, these markers represent between 1 and 22 putative QTL for the respective traits. The minimum phenotypic variance explained by the QTL for the different traits ranged from 15% to 53%. A subset of 27 markers were significantly associated with two or more traits. These markers were predominantly shared between traits that were significantly correlated at the phenotypic level. The results show clearly that in rapeseed, QTL mapping by association analysis is a viable alternative to QTL mapping in segregating populations.     

Comparison of biometrical approaches for QTL detection in multiple segregating families

Detection of QTL in multiple segregating families possesses many advantages over the classical QTL mapping in biparental populations. It has thus become increasingly popular, and different biometrical approaches are available to analyze such data sets. We empirically compared an approach based on linkage mapping methodology with an association mapping approach. To this end, we used a large population of 788 elite maize lines derived from six biparental families genotyped with 857 SNP markers. In addition, we constructed genetic maps with reduced marker densities to assess the dependency of the performance of both mapping approaches on the marker density. We used cross-validation and resample model averaging and found that while association mapping performed better under high marker densities, this was reversed under low marker densities. In addition to main effect QTL, we also detected epistatic interactions. Our results suggest that both approaches will profit from a further increase in marker density and that a cross-validation should be applied irrespective of the biometrical approach.     

Genomic regions determining resistance to leaf stripe (Pyrenophora graminea) in barley.

Leaf stripe is a seed-borne disease of barley (Hordeum vulgare) caused by Pyrenophora graminea. Little is known about the genetics of resistance to this pathogen. In the present work, QTL analysis was applied on two recombinant inbred line (RIL) populations derived from two- and six-rowed barley genotypes with different levels of partial resistance to barley leaf stripe. Quantitative trait loci for partial resistance were identified using the composite interval mapping (CIM) method of PLABQTL software, using the putative QTL markers as cofactors. In the L94 x 'Vada' mapping population, one QTL for resistance was detected on chromosome 2H; the same location as the leaf-stripe resistance gene Rdg1 mapped earlier in 'Alf', where it confers complete resistance to the pathogen. An additional minor-effect QTL was identified by further analyses in this segregating population on chromosome 7H. In L94 x C123, two QTLs for resistance were mapped, one each on chromosomes 7H and 2H.     

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.     

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.     

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.     

Genetic mapping of a 7R Al tolerance QTL in triticale (x Triticosecale Wittmack)

Triticale (x Triticosecale Wittmack) is a relatively new cereal crop. In Poland, triticale is grown on 12 % of arable land (http://www.stat.gov.pl). There is an increasing interest in its cultivation due to lowered production costs and increased adaptation to adverse environmental conditions. However, it has an insufficient tolerance to the presence of aluminum ions (Al³⁺) in the soil. The number of genes controlling aluminum tolerance in triticale and their chromosomal location is not known. Two F2 mapping biparental populations (MP1 and MP15) segregating for aluminum (Al) tolerance were tested with AFLP, SSR, DArT, and specific PCR markers. Genetic mapping enabled the construction of linkage groups representing chromosomes 7R, 5R and 2B. Obtained linkage groups were common for both mapping populations and mostly included the same markers. Composite interval mapping (CIM) allowed identification of a single QTL that mapped to the 7R chromosome and explained 25 % (MP1) and 36 % (MP15) of phenotypic variation. The B1, B26 and Xscm150 markers were 0.04 cM and 0.02 cM from the maximum of the LOD function in the MP1 and MP15, respectively and were highly associated with aluminum tolerance as indicated by Kruskal–Wallis nonparametric test. Moreover, the molecular markers B1, B26, Xrems1162 and Xscm92, previously associated with the Alt4 locus that encoded an aluminum-activated malate transporter (ScALMT1) that was involved in Al tolerance in rye (Secale cereale) also mapped within QTL. Biochemical analysis of plants represented MP1 and MP15 mapping populations confirmed that the QTL located on 7R chromosome in both mapping populations is responsible for Al tolerance.     

Joint-multiple family linkage analysis predicts within-family variation better than single-family analysis of the maize nested association mapping population

Quantitative trait locus (QTL) mapping has been used to dissect the genetic architecture of complex traits and predict phenotypes for marker-assisted selection. Many QTL mapping studies in plants have been limited to one biparental family population. Joint analysis of multiple biparental families offers an alternative approach to QTL mapping with a wider scope of inference. Joint-multiple population analysis should have higher power to detect QTL shared among multiple families, but may have lower power to detect rare QTL. We compared prediction ability of single-family and joint-family QTL analysis methods with fivefold cross-validation for 6 diverse traits using the maize nested association mapping population, which comprises 25 biparental recombinant inbred families. Joint-family QTL analysis had higher mean prediction abilities than single-family QTL analysis for all traits at most significance thresholds, and was always better at more stringent significance thresholds. Most robust QTL (detected in >50% of data samples) were restricted to one family and were often not detected at high frequency by joint-family analysis, implying substantial genetic heterogeneity among families for complex traits in maize. The superior predictive ability of joint-family QTL models despite important genetic differences among families suggests that joint-family models capture sufficient smaller effect QTL that are shared across families to compensate for missing some rare large-effect QTL.     

Efficient Method for Analysis of QTL Using F1 Progenies in an Outcrossing Species

In the present paper, we proposed a statistical procedure based on composite interval mapping for accurate analysis of quantitative trait loci (QTL) for individuals sampled from an outcrossing population with two-generation families consisting of the sampled individuals and F1 progenies obtained by crossing them as parental individuals. In the proposed procedure, haplotypes of markers of parental individuals were reconstructed based on the genotypes of F1 progenies and QTL analyses with composite interval mapping were conducted separately for each of parents as well as jointly for both parents. A least squares method was applied to the composite interval mapping, where some of markers were selected as cofactors to absorb the variation induced by QTL located elsewhere in the genome. The procedure was evaluated for the efficiency in detecting QTL and the precision of estimates of locations and effects of QTL using simulations. It was shown that QTL with interaction between paternal and maternal alleles was effectively detected by joint analysis of both parents, while a QTL segregating only in one parent, closely linked to a QTL segregating only in the other parent, was successfully detected by analyzing separately each of the parents with inclusion of markers of both parents. The proposed procedure can provide detailed genetic information useful for marker assisted breeding in an outcrossing species such as forest trees.     

Genomewide markers as cofactors for precision mapping of quantitative trait loci

In composite interval mapping of quantitative trait loci (QTL), subsets of background markers are used to account for the effects of QTL outside the marker interval being tested. Here, I propose a QTL mapping approach (called G model) that utilizes genomewide markers as cofactors. The G model involves backward elimination on a given chromosome after correcting for genomewide marker effects, calculated under a random effects model, at all the other chromosomes. I simulated a trait controlled by 15 or 30 QTL, mapping populations of N = 96, 192, and 384 recombinant inbreds, and N _M = 192 and 384 evenly spaced markers. In the C model, which utilized subsets of background markers, the number of QTL detected and the number of false positives depended on the number of cofactors used, with five cofactors being too few with N = 384 and 20–40 cofactors being too many with N = 96. A window size of 0 cM for excluding cofactors maintained the number of true QTL detected while decreasing the number of false positives. The number of true QTL detected was generally higher with the G model than with the C model, and the G model led to good control of the type I error rate in simulations where the null hypothesis of no marker–QTL linkage was true. Overall, the results indicated that the G model is useful in QTL mapping because it is less subjective and has equal, if not better, performance when compared with the traditional approach of using subsets of markers to account for background QTL.     

A multiparental cross population for mapping QTL for agronomic traits in durum wheat (Triticum turgidum ssp. durum)

Multiparental cross designs for mapping quantitative trait loci (QTL) provide an efficient alternative to biparental populations because of their broader genetic basis and potentially higher mapping resolution. We describe the development and deployment of a recombinant inbred line (RIL) population in durum wheat (Triticum turgidum ssp. durum) obtained by crossing four elite cultivars. A linkage map spanning 2664 cM and including 7594 single nucleotide polymorphisms (SNPs) was produced by genotyping 338 RILs. QTL analysis was carried out by both interval mapping on founder haplotype probabilities and SNP bi‐allelic tests for heading date and maturity date, plant height and grain yield from four field experiments. Sixteen QTL were identified across environments and detection methods, including two yield QTL on chromosomes 2BL and 7AS, with the former mapped independently from the photoperiod response gene Ppd‐B1, while the latter overlapped with the vernalization locus VRN‐A3. Additionally, 21 QTL with environment‐specific effects were found. Our results indicated a prevalence of environment‐specific QTL with relatively small effect on the control of grain yield. For all traits, functionally different QTL alleles in terms of direction and size of genetic effect were distributed among parents. We showed that QTL results based on founder haplotypes closely matched functional alleles at known heading date loci. Despite the four founders, only 2.1 different functional haplotypes were estimated per QTL, on average. This durum wheat population provides a mapping resource for detailed genetic dissection of agronomic traits in an elite background typical of breeding programmes.     

Combined GWAS and QTL mapping revealed candidate genes and SNP network controlling recovery and tolerance traits associated with drought tolerance in seedling winter wheat

To date, very little research on drought tolerance has been conducted at the seedling stage in winter wheat. In this study, two types of traits, namely tolerance and recovery traits, associated with drought tolerance were scored in biparental mapping population (BPP) and association mapping population (A-set). The results of this study revealed no or weak significant correlation between the two types of traits. Based on GWAS and QTL mapping analyses, all QTLs associated with recovery traits were completely different from those associated with tolerance traits except one QTL in each population that was found to be associated with one tolerance trait and one recovery trait. The analysis of SNP and gene networks confirmed the results of combined GWAS and QTL mapping. One SNP marker located on the 2B chromosome (S2B_26494801) was found to be associated with recovery traits in both populations. The results of this study provided new information on understanding and improving drought tolerance in winter wheat.     

Discovery and validation of genomic regions associated with resistance to maize lethal necrosis in four biparental populations

In sub-Saharan Africa, maize is the key determinant of food security for smallholder farmers. The sudden outbreak of maize lethal necrosis (MLN) disease is seriously threatening the maize production in the region. Understanding the genetic basis of MLN resistance is crucial. In this study, we used four biparental populations applied linkage mapping and joint linkage mapping approaches to identify and validate the MLN resistance-associated genomic regions. All populations were genotyped with low to high density markers and phenotyped in multiple environments against MLN under artificial inoculation. Phenotypic variation for MLN resistance was significant and heritability was moderate to high in all four populations for both early and late stages of disease infection. Linkage mapping revealed three major quantitative trait loci (QTL) on chromosomes 3, 6, and 9 that were consistently detected in at least two of the four populations. Phenotypic variance explained by a single QTL in each population ranged from 3.9% in population 1 to 43.8% in population 2. Joint linkage association mapping across three populations with three biometric models together revealed 16 and 10 main effect QTL for MLN-early and MLN-late, respectively. The QTL identified on chromosomes 3, 5, 6, and 9 were consistent with the QTL identified by linkage mapping. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed high accuracy for prediction across populations for both MLN-early and MLN-late. Overall, the study discovered and validated the presence of major effect QTL on chromosomes 3, 6, and 9 which can be potential candidates for marker-assisted breeding to improve the MLN resistance.     

Genotypic correlations and QTL correspondence between line per se and testcross performance in sugar beet (Beta vulgaris L.) for the three agronomic traits beet yield,potassium content,and sodium content

The genotypic correlation between line per se and testcross performance is an important quantitative genetic parameter in the design of hybrid breeding programs. The main goal of this survey was to study the association of line per se and testcross performance at the phenotypic and molecular levels by applying multiple-line cross quantitative trait locus (QTL) mapping. We used experimental data from line per se and testcross performance of three segregating sugar beet (Beta vulgaris L.) populations. The segregating progenies were genotyped with 481 single nucleotide polymorphism and 40 simple sequence repeat markers and evaluated in field trials for beet yield as well as potassium and sodium content. We observed a decrease in the genotypic correlations between testcross and line per se performance with increasing complexity of the analyzed trait. This picture was also reflected at a molecular level by the presence of overlapping QTLs. A more detailed analysis of the forces causing low genotypic correlation between line per se and testcross performance could not rule out a possible relevance of epistasis and suggested the presence of masking dominance effects.     

Pooled analysis of data from multiple quantitative trait locus mapping populations

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F_2:3 mapping populations, Hamilton (susceptible) × PI 90763 (resistant) and Magellan (susceptible) × PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.     

Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis

QTL detection experiments in livestock species commonly use the half-sib design. Each male is mated to a number of females, each female producing a limited number of progeny. Analysis consists of attempting to detect associations between phenotype and genotype measured on the progeny. When family sizes are limiting experimenters may wish to incorporate as much information as possible into a single analysis. However, combining information across sires is problematic because of incomplete linkage disequilibrium between the markers and the QTL in the population. This study describes formulæ for obtaining MLEs via the expectation maximization (EM) algorithm for use in a multiple-trait, multiple-family analysis. A model specifying a QTL with only two alleles, and a common within sire error variance is assumed. Compared to single-family analyses, power can be improved up to fourfold with multi-family analyses. The accuracy and precision of QTL location estimates are also substantially improved. With small family sizes, the multi-family, multi-trait analyses reduce substantially, but not totally remove, biases in QTL effect estimates. In situations where multiple QTL alleles are segregating the multi-family analysis will average out the effects of the different QTL alleles.