Optimum breeding strategies using genomic and phenotypic selection for the simultaneous improvement of two traits.

KEY MESSAGE: A breeding strategy combining genomic with one-stage phenotypic selection maximizes annual selection gain for net merit. Choice of the selection index strongly affects the selection gain expected in individual traits. Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package "selectiongain" from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔGa) for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategy GSrapid (one-stage GS followed by one-stage phenotypic selection) achieved higher ΔGa than classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith-Hazel or base index delivered higher ΔGa for net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lower ΔGa in net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package "selectiongain" with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.

Testcross performance of doubled haploid lines from European flint maize landraces is promising for broadening the genetic base of elite germplasm.

KEY MESSAGE: Selected doubled haploid lines averaged similar testcross performance as their original landraces, and the best of them approached the yields of elite inbreds, demonstrating their potential to broaden the narrow genetic diversity of the flint germplasm pool. Maize landraces represent a rich source of genetic diversity that remains largely idle because the high genetic load and performance gap to elite germplasm hamper their use in modern breeding programs. Production of doubled haploid (DH) lines can mitigate problems associated with the use of landraces in pre-breeding. Our objective was to assess in comparison with modern materials the testcross performance (TP) of the best 89 out of 389 DH lines developed from six landraces and evaluated in previous studies for line per se performance (LP). TP with a dent tester was evaluated for the six original landraces, ~ 15 DH lines from each landrace selected for LP, and six elite flint inbreds together with nine commercial hybrids for grain and silage traits. Mean TP of the DH lines rarely differed significantly from TP of their corresponding landrace, which averaged in comparison with the mean TP of the elite flint inbreds ~ 20% lower grain yield and ~ 10% lower dry matter and methane yield. Trait correlations of DH lines closely agreed with the literature; correlation of TP with LP was zero for grain yield, underpinning the need to evaluate TP in addition to LP. For all traits, we observed substantial variation for TP among the DH lines and the best showed similar TP yields as the elite inbreds. Our results demonstrate the high potential of landraces for broadening the narrow genetic base of the flint heterotic pool and the usefulness of the DH technology for exploiting idle genetic resources from gene banks.

Marker-Assisted Breeding of Improved Maternal Haploid Inducers in Maize for the Tropical/Subtropical Regions.

For efficient production of doubled haploid (DH) lines in maize, maternal haploid inducer lines with high haploid induction rate (HIR) and good adaptation to the target environments is an important requirement. In this study, we present second-generation Tropically Adapted Inducer Lines (2GTAILs), developed using marker assisted selection (MAS) for qhir1, a QTL with a significant positive effect on HIR from the crosses between elite tropical maize inbreds and first generation Tropically Adapted Inducers Lines (TAILs). Evaluation of 2GTAILs for HIR and agronomic performance in the tropical and subtropical environments indicated superior performance of 2GTAILs over the TAILs for both HIR and agronomic performance, including plant vigor, delayed flowering, grain yield, and resistance to ear rots. One of the new inducers 2GTAIL006 showed an average HIR of 13.1% which is 48.9% higher than the average HIR of the TAILs. Several other 2GTAILs also showed higher HIR compared to the TAILs. While employing MAS for qhir1 QTL, we observed significant influence of the non-inducer parent on the positive effect of qhir1 QTL on HIR. The non-inducer parents that resulted in highest mean HIR in the early generation qhir1+ families also gave rise to highest numbers of candidate inducers, some of which showed transgressive segregation for HIR. The mean HIR of early generation qhir1+ families involving different non-inducer parents can potentially indicate recipient non-inducer parents that can result in progenies with high HIR. Our study also indicated that the HIR associated traits (endosperm abortion rate, embryo abortion rate, and proportion of haploid plants among the inducer plants) can be used to differentiate inducers vs. non-inducers but are not suitable for differentiating inducers with varying levels of haploid induction rates. We propose here an efficient methodology for developing haploid inducer lines combining MAS for qhir1 with HIR associated traits.

Omics-based hybrid prediction in maize.

KEY MESSAGE: Complementing genomic data with other "omics" predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits. Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream "omics" data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of "omics" data. Here, we evaluate several "omics" predictors-genomic, transcriptomic and metabolic data-measured on parent lines at early developmental stages and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream "omics" data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.

Tapping the genetic diversity of landraces in allogamous crops with doubled haploid lines: a case study from European flint maize.

KEY MESSAGE: Using landraces for broadening the genetic base of elite maize germplasm is hampered by heterogeneity and high genetic load. Production of DH line libraries can help to overcome these problems. Landraces of maize (Zea mays L.) represent a huge reservoir of genetic diversity largely untapped by breeders. Genetic heterogeneity and a high genetic load hamper their use in hybrid breeding. Production of doubled haploid line libraries (DHL) by the in vivo haploid induction method promises to overcome these problems. To test this hypothesis, we compared the line per se performance of 389 doubled haploid (DH) lines across six DHL produced from European flint landraces with that of four flint founder lines (FFL) and 53 elite flint lines (EFL) for 16 agronomic traits evaluated in four locations. The genotypic variance ([Formula: see text]) within DHL was generally much larger than that among DHL and exceeded also [Formula: see text] of the EFL. For most traits, the means and [Formula: see text] differed considerably among the DHL, resulting in different expected selection gains. Mean grain yield of the EFL was 25 and 62% higher than for the FFL and DHL, respectively, indicating considerable breeding progress in the EFL and a remnant genetic load in the DHL. Usefulness of the best 20% lines was for individual DHL comparable to the EFL and grain yield (GY) in the top lines from both groups was similar. Our results corroborate the tremendous potential of landraces for broadening the narrow genetic base of elite germplasm. To make best use of these "gold reserves", we propose a multi-stage selection approach with optimal allocation of resources to (1) choose the most promising landraces for DHL production and (2) identify the top DH lines for further breeding.

Low validation rate of quantitative trait loci for Gibberella ear rot resistance in European maize.

KEY MESSAGE: Six quantitative trait loci (QTL) for Gibberella ear rot resistance in maize were tested in two different genetic backgrounds; three QTL displayed an effect in few near isogenic line pairs. Few quantitative trait loci (QTL) mapping studies for Gibberella ear rot (GER) have been conducted, but no QTL have been verified so far. QTL validation is prudent before their implementation into marker-assisted selection (MAS) programs. Our objectives were to (1) validate six QTL for GER resistance, (2) evaluate the QTL across two genetic backgrounds, (3) investigate the genetic background outside the targeted introgressions. Pairs of near isogenic lines (NILs) segregating for a single QTL (Qger1, Qger2, Qger10, Qger13, Qger16, or Qger21) were developed by recurrent backcross until generation BC3S2. Donor parents (DP) carrying QTL were backcrossed to a susceptible (UH009) and a moderately resistant (UH007) recurrent parent. MAS was performed using five SNP markers covering a region of 40 cM around each QTL. All NILs were genotyped with the MaizeSNP50 assay and phenotyped for GER severity and deoxynivalenol and zearalenone content. Traits were significantly (P < 0.001) intercorrelated. Out of 34 NIL pairs with the UH009 genetic background, three pairs showed significant differences in at least one trait for three QTL (Qger1, Qger2, Qger13). Out of 25 NIL pairs with the UH007 genetic background, five pairs showed significant differences in at least one trait for two QTL (Qger2, Qger21). However, Qger16, Qger10 and Qger13 were most likely false positives. The genetic background possibly affected NIL pairs comparisons due to linkage drag and/or epistasis with residual loci from the DP in non-target regions. In conclusion, validation rates were disappointingly low, which further indicates that GER resistance is controlled by many low-effect QTL.

Outlier detection methods for generalized lattices: a case study on the transition from ANOVA to REML.

KEY MESSAGE: We review and propose several methods for identifying possible outliers and evaluate their properties. The methods are applied to a genomic prediction program in hybrid rye. Many plant breeders use ANOVA-based software for routine analysis of field trials. These programs may offer specific in-built options for residual analysis that are lacking in current REML software. With the advance of molecular technologies, there is a need to switch to REML-based approaches, but without losing the good features of outlier detection methods that have proven useful in the past. Our aims were to compare the variance component estimates between ANOVA and REML approaches, to scrutinize the outlier detection method of the ANOVA-based package PlabStat and to propose and evaluate alternative procedures for outlier detection. We compared the outputs produced using ANOVA and REML approaches of four published datasets of generalized lattice designs. Five outlier detection methods are explained step by step. Their performance was evaluated by measuring the true positive rate and the false positive rate in a dataset with artificial outliers simulated in several scenarios. An implementation of genomic prediction using an empirical rye multi-environment trial was used to assess the outlier detection methods with respect to the predictive abilities of a mixed model for each method. We provide a detailed explanation of how the PlabStat outlier detection methodology can be translated to REML-based software together with the evaluation of alternative methods to identify outliers. The method combining the Bonferroni-Holm test to judge each residual and the residual standardization strategy of PlabStat exhibited good ability to detect outliers in small and large datasets and under a genomic prediction application. We recommend the use of outlier detection methods as a decision support in the routine data analyses of plant breeding experiments.

Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize.

KEY MESSAGE: QTL analysis for Fusarium resistance traits with multiple connected families detected more QTL than single-family analysis. Prediction accuracy was tightly associated with the kinship of the validation and training set. ABSTRACT: QTL mapping has recently shifted from analysis of single families to multiple, connected families and several biometric models have been suggested. Using a high-density consensus map with 2472 marker loci, we performed QTL mapping with five connected bi-parental families with 639 doubled-haploid (DH) lines in maize for ear rot resistance and analyzed traits DON, Gibberella ear rot severity (GER), and days to silking (DS). Five biometric models differing in the assumption about the number and effects of alleles at QTL were compared. Model 2 to 5 performing joint analyses across all families and using linkage and/or linkage disequilibrium (LD) information identified all and even further QTL than Model 1 (single-family analyses) and generally explained a higher proportion pG of the genotypic variance for all three traits. QTL for DON and GER were mostly family specific, but several QTL for DS occurred in multiple families. Many QTL displayed large additive effects and most alleles increasing resistance originated from a resistant parent. Interactions between detected QTL and genetic background (family) occurred rarely and were comparatively small. Detailed analysis of three fully connected families yielded higher pG values for Model 3 or 4 than for Model 2 and 5, irrespective of the size NTS of the training set (TS). In conclusion, Model 3 and 4 can be recommended for QTL-based prediction with larger families. Including a sufficiently large number of full sibs in the TS helped to increase QTL-based prediction accuracy (rVS) for various scenarios differing in the composition of the TS.

Forecasting the accuracy of genomic prediction with different selection targets in the training and prediction set as well as truncation selection.

KEY MESSAGE: Deterministic formulas accurately forecast the decline in predictive ability of genomic prediction with changing testers, target environments or traits and truncation selection. Genomic prediction of testcross performance (TP) was found to be a promising selection tool in hybrid breeding as long as the same tester and environments are used in the training and prediction set. In practice, however, selection targets often change in terms of testers, target environments or traits leading to a reduced predictive ability. Hence, it would be desirable to estimate for given training data the expected decline in the predictive ability of genomic prediction under such settings by deterministic formulas that require only quantitative genetic parameters available from the breeding program. Here, we derived formulas for forecasting the predictive ability under different selection targets in the training and prediction set and applied these to predict the TP of lines based on line per se or testcross evaluations. On the basis of two experiments with maize, we validated our approach in four scenarios characterized by different selection targets. Forecasted and empirically observed predictive abilities obtained by cross-validation generally agreed well, with deviations between -0.06 and 0.01 only. Applying the prediction model to a different tester and/or year reduced the predictive ability by not more than 18%. Accounting additionally for truncation selection in our formulas indicated a substantial reduction in predictive ability in the prediction set, amounting, e.g., to 53% for a selected fraction α = 10%. In conclusion, our deterministic formulas enable forecasting the predictive abilities of new selection targets with sufficient precision and could be used to calculate parameters required for optimizing the allocation of resources in multi-stage genomic selection.

High-density genotyping: an overkill for QTL mapping? Lessons learned from a case study in maize and simulations.

High-density genotyping is extensively exploited in genome-wide association mapping studies and genomic selection in maize. By contrast, linkage mapping studies were until now mostly based on low-density genetic maps and theoretical results suggested this to be sufficient. This raises the question, if an increase in marker density would be an overkill for linkage mapping in biparental populations, or if important QTL mapping parameters would benefit from it. In this study, we addressed this question using experimental data and a simulation based on linkage maps with marker densities of 1, 2, and 5 cM. QTL mapping was performed for six diverse traits in a biparental population with 204 doubled haploid maize lines and in a simulation study with varying QTL effects and closely linked QTL for different population sizes. Our results showed that high-density maps neither improved the QTL detection power nor the predictive power for the proportion of explained genotypic variance. By contrast, the precision of QTL localization, the precision of effect estimates of detected QTL, especially for small and medium sized QTL, as well as the power to resolve closely linked QTL profited from an increase in marker density from 5 to 1 cM. In conclusion, the higher costs for high-density genotyping are compensated for by more precise estimates of parameters relevant for knowledge-based breeding, thus making an increase in marker density for linkage mapping attractive.

Best linear unbiased prediction and optimum allocation of test resources in maize breeding with doubled haploids.

With best linear unbiased prediction (BLUP), information from genetically related candidates is combined to obtain more precise estimates of genotypic values of test candidates and thereby increase progress from selection. We developed and applied theory and Monte Carlo simulations implementing BLUP in 2 two-stage maize breeding schemes and various selection strategies. Our objectives were to (1) derive analytical solutions of the mixed model equations under two breeding schemes, (2) determine the optimum allocation of test resources with BLUP under different assumptions regarding the variance component ratios for grain yield in maize, (3) compare the progress from selection using BLUP and conventional phenotypic selection based on mean performance solely of the candidates, and (4) analyze the potential of BLUP for further improving the progress from selection. The breeding schemes involved selection for testcross performance either of DH lines at both stages (DHTC) or of S(1) families at the first stage and DH lines at the second stage (S(1)TC-DHTC). Our analytical solutions allowed much faster calculations of the optimum allocations and superseded matrix inversions to solve the mixed model equations. Compared to conventional phenotypic selection, the progress from selection was slightly higher with BLUP for both optimization criteria, namely the selection gain and the probability to select the best genotypes. The optimum allocation of test resources in S(1)TC-DHTC involved ≥ 10 test locations at both stages, a low number of crosses (≤ 6) each with 100-300 S(1) families at the first stage, and 500-1,000 DH lines at the second stage. In breeding scheme DHTC, the optimum number of test candidates at the first stage was 5-10 times larger, whereas the number of test locations at the first stage and the number of test candidates at the second stage were strongly reduced compared to S(1)TC-DHTC.

Optimum allocation of resources for QTL detection using a nested association mapping strategy in maize.

In quantitative trait locus (QTL) mapping studies, it is mandatory that the available financial resources are spent in such a way that the power for detection of QTL is maximized. The objective of this study was to optimize for three different fixed budgets the power of QTL detection 1 - beta* in recombinant inbred line (RIL) populations derived from a nested design by varying (1) the genetic complexity of the trait, (2) the costs for developing, genotyping, and phenotyping RILs, (3) the total number of RILs, and (4) the number of environments and replications per environment used for phenotyping. Our computer simulations were based on empirical data of 653 single nucleotide polymorphism markers of 26 diverse maize inbred lines which were selected on the basis of 100 simple sequence repeat markers out of a worldwide sample of 260 maize inbreds to capture the maximum genetic diversity. For the standard scenario of costs, the optimum number of test environments (E (opt)) ranged across the examined total budgets from 7 to 19 in the scenarios with 25 QTL. In comparison, the E (opt) values observed for the scenarios with 50 and 100 QTL were slightly higher. Our finding of differences in 1 - beta* estimates between experiments with optimally and sub-optimally allocated resources illustrated the potential to improve the power for QTL detection without increasing the total resources necessary for a QTL mapping experiment. Furthermore, the results of our study indicated that also in studies using the latest genomics tools to dissect quantitative traits, it is required to evaluate the individuals of the mapping population in a high number of environments with a high number of replications per environment.

High congruency of QTL positions for heterosis of grain yield in three crosses of maize.

The genetic basis of heterosis in maize has been investigated in a number of studies but results have not been conclusive. Here, we compare quantitative trait loci (QTL) mapping results for grain yield, grain moisture, and plant height from three populations derived from crosses of the heterotic pattern Iowa Stiff Stalk Synthetic x Lancaster Sure Crop, investigated with the Design III, and analyzed with advanced statistical methods specifically developed to examine the genetic basis of mid-parent heterosis (MPH). In two populations, QTL analyses were conducted with a joint fit of linear transformations Z (1) (trait mean across pairs of backcross progenies) and Z (2) (half the trait difference between pairs of backcross progenies) to estimate augmented additive and augmented dominance effects of each QTL, as well as their ratio. QTL results for the third population were obtained from the literature. For Z (2) of grain yield, congruency of QTL positions was high across populations, and a large proportion of the genetic variance (~70%) was accounted for by QTL. This was not the case for Z (1) or the other two traits. Further, almost all congruent grain yield QTL were located in the same or an adjacent bin encompassing the centromere. We conclude that different alleles have been fixed in each heterotic pool, which in combination with allele(s) from the opposite heterotic pool lead to high MPH for grain yield. Their positive interactions very likely form the base line for the superior performance of the heterotic pattern under study.

Hybrid maize breeding with doubled haploids: V. Selection strategies for testcross performance with variable sizes of crosses and S(1) families.

In hybrid maize (Zea mays L.) breeding, doubled haploids (DH) are increasingly replacing inbreds developed by recurrent selfing. Doubled haploids may be developed directly from S(0) plants in the parental cross or via S(1) families. In both these breeding schemes, we examined 2 two-stage selecting strategies, i.e., considering or ignoring cross and family structure while selection among and within parental crosses and S(1) families. We examined the optimum allocation of resources to maximize the selection gain DeltaG and the probability P(q) of identifying the q% best genotypes. Our specific objectives were to (1) determine the optimum number and size of crosses and S(1) families, as well as the optimum number of test environments and (2) identify the superior selection strategy. Selection was based on the evaluation of testcross progenies of (1) DH lines in both stages (DHTC) and (2) S(1) families in the first stage and of DH lines within S(1) families in the second stage (S(1)TC-DHTC) with uniform and variable sizes of crosses and S(1) families. We developed and employed simulation programs for selection with variable sizes of crosses and S(1) families within crosses. The breeding schemes and selection strategies showed similar relative efficiency for both optimization criteria DeltaG and P (0.1%). As compared with DHTC, S(1)TC-DHTC had larger DeltaG and P (0.1%), but a higher standard deviation of DeltaG. The superiority of S(1)TC-DHTC was increased when the selection was done among all DH lines ignoring their cross and family structure and using variable sizes of crosses and S(1) families. In DHTC, the best selection strategy was to ignore cross structures and use uniform size of crosses.

Broadening the genetic base of European maize heterotic pools with US Cornbelt germplasm using field and molecular marker data.

Maize (Zea mays L.) breeders are concerned about the narrowing of the genetic base of elite germplasm. To reverse this trend, elite germplasm from other geographic regions can be introgressed, but due to lack of adaptation it is difficult to assess their breeding potential in the targeted environment. The objectives of this study were to (1) investigate the relationship between European and US maize germplasm, (2) examine the suitability of different mega-environments and measures of performance to assess the breeding potential of exotics, and (3) study the relationship of genetic distance with mid-parent heterosis (MPH). Eight European inbreds from the Dent and Flint heterotic groups, 11 US inbreds belonging to Stiff Stalk (SS), non-Stiff Stalk (NSS), and CIMMYT Pool 41, and their 88 factorial crosses in F(1) and F(2) generations were evaluated for grain yield and dry matter concentration. The experiments were conducted in three mega-environments: Central Europe (target mega-environment), US Cornbelt (mega-environment where donor lines were developed), and Southeast Europe (an intermediate mega-environment). The inbreds were also fingerprinted with 266 SSR markers. Suitable criteria to identify promising exotic germplasm were F(1) hybrid performance in the targeted mega-environment and F(1) and parental performance in the intermediate mega-environment. Marker-based genetic distances reflected relatedness among the inbreds, but showed no association with MPH. Based on genetic distance, MPH, and F(1) performance, we suggest to introgress SS germplasm into European Dents and NSS into European Flints, in order to exploit the specific adaptation of European flint germplasm and the excellent combining ability of US germplasm in European maize breeding programs.

Hybrid maize breeding with doubled haploids. IV. Number versus size of crosses and importance of parental selection in two-stage selection for testcross performance.

Parental selection influences the gain from selection and the optimum allocation of test resources in breeding programs. We compared two hybrid maize (Zea mays L.) breeding schemes with evaluation of testcross progenies: (a) doubled haploid (DH) lines in both stages (DHTC) and (b) S(1) families in the first stage and DH lines within S(1) families in the second stage (S(1)TC-DHTC). Our objectives were to (1) determine the optimum allocation regarding the number of crosses, S(1) families, DH lines, and test locations, (2) investigate the impact of parental selection on the optimum allocation and selection gain (DeltaG), and (3) compare the maximum DeltaG achievable with each breeding scheme. Selection gain was calculated by numerical integration. Different assumptions were made regarding the budget, variance components, correlation between the mean phenotypic performance of the parents and the mean genotypic value of the testcross performance of their progenies (rho( P )), and the composition of the finally selected test candidates. In comparison with randomly chosen crosses, maximum DeltaG was largely increased with parental selection in both breeding schemes. With an increasing correlation rho( P ), this superiority increased strongly, while the optimum number of crosses decreased in favor of an increased number of test candidates within crosses. Thus, concentration on few crosses among the best parental lines might be a promising approach for short-term success in advanced cycle breeding. Breeding scheme S(1)TC-DHTC led to a larger DeltaG but had a longer cycle length than DHTC. However, with further improvements in the DH technique and the realization of more than two generations per year, early testing of S(1) families prior to production of DH lines would become very attractive in hybrid maize breeding.

Genetic basis of heterosis for growth-related traits in Arabidopsis investigated by testcross progenies of near-isogenic lines reveals a significant role of epistasis.

Epistasis seems to play a significant role in the manifestation of heterosis. However, the power of detecting epistatic interactions among quantitative trait loci (QTL) in segregating populations is low. We studied heterosis in Arabidopsis thaliana hybrid C24 x Col-0 by testing near-isogenic lines (NILs) and their triple testcross (TTC) progenies. Our objectives were to (i) provide the theoretical basis for estimating different types of genetic effects with this experimental design, (ii) determine the extent of heterosis for seven growth-related traits, (iii) map the underlying QTL, and (iv) determine their gene action. Two substitution libraries, each consisting of 28 NILs and covering approximately 61 and 39% of the Arabidopsis genome, were assayed by 110 single-nucleotide polymorphism (SNP) markers. With our novel generation means approach 38 QTL were detected, many of which confirmed heterotic QTL detected previously in the same cross with TTC progenies of recombinant inbred lines. Furthermore, many of the QTL were common for different traits and in common with the 58 QTL detected by a method that compares triplets consisting of a NIL, its recurrent parent, and their F(1) cross. While the latter approach revealed mostly (75%) overdominant QTL, the former approach allowed separation of dominance and epistasis by analyzing all materials simultaneously and yielded substantial positive additive x additive effects besides directional dominance. Positive epistatic effects reduced heterosis for growth-related traits in our materials.

Heterosis for biomass-related traits in Arabidopsis investigated by quantitative trait loci analysis of the triple testcross design with recombinant inbred lines.

Arabidopsis thaliana has emerged as a leading model species in plant genetics and functional genomics including research on the genetic causes of heterosis. We applied a triple testcross (TTC) design and a novel biometrical approach to identify and characterize quantitative trait loci (QTL) for heterosis of five biomass-related traits by (i) estimating the number, genomic positions, and genetic effects of heterotic QTL, (ii) characterizing their mode of gene action, and (iii) testing for presence of epistatic effects by a genomewide scan and marker x marker interactions. In total, 234 recombinant inbred lines (RILs) of Arabidopsis hybrid C24 x Col-0 were crossed to both parental lines and their F1 and analyzed with 110 single-nucleotide polymorphism (SNP) markers. QTL analyses were conducted using linear transformations Z1, Z2, and Z3 calculated from the adjusted entry means of TTC progenies. With Z1, we detected 12 QTL displaying augmented additive effects. With Z2, we mapped six QTL for augmented dominance effects. A one-dimensional genome scan with Z3 revealed two genomic regions with significantly negative dominance x additive epistatic effects. Two-way analyses of variance between marker pairs revealed nine digenic epistatic interactions: six reflecting dominance x dominance effects with variable sign and three reflecting additive x additive effects with positive sign. We conclude that heterosis for biomass-related traits in Arabidopsis has a polygenic basis with overdominance and/or epistasis being presumably the main types of gene action.

Hybrid maize breeding with doubled haploids: III. Efficiency of early testing prior to doubled haploid production in two-stage selection for testcross performance.

Early testing prior to doubled haploid (DH) production is a promising approach in hybrid maize breeding. We (1) determined the optimum allocation of the number of S(1) families, DH lines, and test locations for two different breeding schemes, (2) compared the maximum selection gain achievable under both breeding schemes, and (3) investigated limitations in the current method of DH production. Selection gain was calculated by numerical integration in two-stage breeding schemes with evaluation of testcross progenies of (1) DH lines in both stages (DHTC), or (2) S(1) families in the first and DH lines within S(1) families in the second stage (S(1)TC-DHTC). Different assumptions were made regarding the budget, variance components, and time of DH production within S(1) families. Maximum selection gain in S(1)TC-DHTC was about 10% larger than in DHTC, indicating the large potential of early testing prior to DH production. The optimum allocation of test resources in S(1)TC-DHTC involved similar numbers of test locations and test candidates in both stages resulting in a large optimum number of S(1) families in the first stage and DH lines within the best two S(1) families in the second stage. The longer cycle length of S(1)TC-DHTC can be compensated by haploid induction of individual S(1) plants instead of S(1) families. However, this reduces selection gain largely due to the current limitations in the DH technique. Substantial increases in haploid induction and chromosome doubling rates as well as reduction in costs of DH production would allow early testing of S(1) lines and subsequent production and testing of DH lines in a breeding scheme that combines high selection gain with a short cycle length.

Analysis of a triple testcross design with recombinant inbred lines reveals a significant role of epistasis in heterosis for biomass-related traits in Arabidopsis.

Primary causes of heterosis are still unknown. Our goal was to investigate the extent and underlying genetic causes of heterosis for five biomass-related traits in Arabidopsis thaliana. We (i) investigated the relative contribution of dominance and epistatic effects to heterosis in the hybrid C24 x Col-0 by generation means analysis and estimates of variance components based on a triple testcross (TTC) design with recombinant inbred lines (RILs), (ii) estimated the average degree of dominance, and (iii) examined the importance of reciprocal and maternal effects in this cross. In total, 234 RILs were crossed to parental lines and their F1's. Midparent heterosis (MPH) was high for rosette diameter at 22 days after sowing (DAS) and 29 DAS, growth rate (GR), and biomass yield (BY). Using the F2-metric, directional dominance prevailed for the majority of traits studied but reciprocal and maternal effects were not significant. Additive and dominance variances were significant for all traits. Additive x additive and dominance x dominance variances were significant for all traits but GR. We conclude that dominance as well as digenic and possibly higher-order epistatic effects play an important role in heterosis for biomass-related traits. Our results encourage the use of Arabidopsis hybrid C24 x Col-0 for identification and description of quantitative trait loci (QTL) for heterosis for biomass-related traits and further genomic studies.