Manje Gowda

Accuracy of genomic selection in European maize elite breeding populations

Genomic selection is a promising breeding strategy for rapid improvement of complex traits. The objective of our study was to investigate the prediction accuracy of genomic breeding values through cross validation. The study was based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program. The plants were intensively phenotyped in multi-location field trials and fingerprinted with 960 SNP markers. We used random regression best linear unbiased prediction in combination with fivefold cross validation. The prediction accuracy across populations was higher for grain moisture (0.90) than for grain yield (0.58). The accuracy of genomic selection realized for grain yield corresponds to the precision of phenotyping at unreplicated field trials in 3–4 locations. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs.

Hybrid breeding in autogamous cereals

Hybrid breeding in autogamous cereals has a long history of attempts with moderate success. There is a vast amount of literature investigating the potential problems and solutions, but until now, market share of hybrids is still a niche compared to line varieties. Our aim was to summarize the status quo of hybrid breeding efforts for the autogamous cereals wheat, rice, barley, and triticale. Furthermore, the research needs for a successful hybrid breeding in autogamous cereals are intensively discussed. To our opinion, the basic requirements for a successful hybrid breeding in autogamous cereals are fulfilled. Nevertheless, optimization of the existing hybridization systems is urgently required and should be coupled with the development of clear male and female pool concepts. We present a quantitative genetic framework as a first step to compare selection gain of hybrid versus line breeding. The lack of precise empirical estimates of relevant quantitative genetic parameters, however, is currently the major bottleneck for a robust evaluation of the potential of hybrid breeding in autogamous cereals.

Association mapping for quality traits in soft winter wheat

Improvement of end-use quality in bread wheat (Triticum aestivum L.) depends on a thorough understanding of the genetic basis of important quality traits. The main goal of our study was to investigate the genetic basis of 1,000-kernel weight, protein content, sedimentation volume, test weight, and starch concentration using an association mapping approach. We fingerprinted 207 diverse European elite soft winter wheat lines with 115 SSR markers and evaluated the genotypes in multi-environment trials. The principal coordinate analysis revealed absence of a clear population but presence of a family structure. Therefore, we used linear mixed models and marker-based kinship matrices to correct for family structure. In genome-wide scans, we detected main effect QTL for all five traits. In contrast, epistatic QTL were only observed for sedimentation volume and test weight explaining a small proportion of the genotypic variation. Consequently, our findings suggested that integrating epistasis in marker-assisted breeding will not lead to substantially increased selection gain for quality traits in soft winter wheat.

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.

Comparison of biometrical models for joint linkage association mapping.

Joint linkage association mapping (JLAM) combines the advantages of linkage mapping and association mapping, and is a powerful tool to dissect the genetic architecture of complex traits. The main goal of this study was to use a cross-validation strategy, resample model averaging and empirical data analyses to compare seven different biometrical models for JLAM with regard to the correction for population structure and the quantitative trait loci (QTL) detection power. Three linear models and four linear mixed models with different approaches to control for population stratification were evaluated. Models A, B and C were linear models with either cofactors (Model-A), or cofactors and a population effect (Model-B), or a model in which the cofactors and the single-nucleotide polymorphism effect were modeled as nested within population (Model-C). The mixed models, D, E, F and G, included a random population effect (Model-D), or a random population effect with defined variance structure (Model-E), a kinship matrix defining the degree of relatedness among the genotypes (Model-F), or a kinship matrix and principal coordinates (Model-G). The tested models were conceptually different and were also found to differ in terms of power to detect QTL. Model-B with the cofactors and a population effect, effectively controlled population structure and possessed a high predictive power. The varying allele substitution effects in different populations suggest as a promising strategy for JLAM to use Model-B for the detection of QTL and then to estimate their effects by applying Model-C.

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.

Genome-based establishment of a high-yielding heterotic pattern for hybrid wheat breeding

Significance Selfing species wheat are bred as pure-line varieties with stagnating yield growths. In contrast, selection gain in maize is high, owing to massive investment sustained by hybrid seed sales, coupled with an efficient exploitation of hybrid vigor. We have developed a three-step strategy for establishing a heterotic pattern, which was one of the central unsolved challenges for initiating hybrid breeding programs. The benefits of our approach are demonstrated using data for wheat, but the strategy is relevant for several autogamous crops. Our three-step approach facilitates identification of a heterotic pattern, and thus may contribute to meeting the global challenge of increasing demand for food, feed, and fuel. Hybrid breeding promises to boost yield and stability. The single most important element in implementing hybrid breeding is the recognition of a high-yielding heterotic pattern. We have developed a three-step strategy for identifying heterotic patterns for hybrid breeding comprising the following elements. First, the full hybrid performance matrix is compiled using genomic prediction. Second, a high-yielding heterotic pattern is searched based on a developed simulated annealing algorithm. Third, the long-term success of the identified heterotic pattern is assessed by estimating the usefulness, selection limit, and representativeness of the heterotic pattern with respect to a defined base population. This three-step approach was successfully implemented and evaluated using a phenotypic and genomic wheat dataset comprising 1,604 hybrids and their 135 parents. Integration of metabolomic-based prediction was not as powerful as genomic prediction. We show that hybrid wheat breeding based on the identified heterotic pattern can boost grain yield through the exploitation of heterosis and enhance recurrent selection gain. Our strategy represents a key step forward in hybrid breeding and is relevant for self-pollinating crops, which are currently shifting from pure-line to high-yielding and resilient hybrid varieties.

Dissecting the genetic architecture of frost tolerance in Central European winter wheat.

Abiotic stress tolerance in plants is pivotal to increase yield stability, but its genetic basis is still poorly understood. To gain insight into the genetic architecture of frost tolerance, this work evaluated a large mapping population of 1739 wheat (Triticum aestivum L.) lines and hybrids adapted to Central Europe in field trials in Germany and fingerprinted the lines with a 9000 single-nucleotide polymorphism array. Additive effects prevailed over dominance effects. A two-dimensional genome scan revealed the presence of epistatic effects. Genome-wide association mapping in combination with a robust cross-validation strategy identified one frost tolerance locus with a major effect located on chromosome 5B. This locus was not in linkage disequilibrium with the known frost loci Fr-B1 and Fr-B2. The use of the detected diagnostic markers on chromosome 5B, however, does not allow prediction of frost tolerance with high accuracy. Application of genome-wide selection approaches that take into account also loci with small effect sizes considerably improved prediction of the genetic variation of frost tolerance in wheat. The developed prediction model is valuable for improving frost tolerance because this trait displays a wide variation in occurrence across years and is therefore a difficult target for conventional phenotypic selection.

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives

Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.

Relatedness severely impacts accuracy of marker-assisted selection for disease resistance in hybrid wheat.

The accuracy of genomic selection depends on the relatedness between the members of the set in which marker effects are estimated based on evaluation data and the types for which performance is predicted. Here, we investigate the impact of relatedness on the performance of marker-assisted selection for fungal disease resistance in hybrid wheat. A large and diverse mapping population of 1739 elite European winter wheat inbred lines and hybrids was evaluated for powdery mildew, leaf rust and stripe rust resistance in multi-location field trials and fingerprinted with 9 k and 90 k SNP arrays. Comparison of the accuracies of prediction achieved with data sets from the two marker arrays revealed a crucial role for a sufficiently high marker density in genome-wide association mapping. Cross-validation studies using test sets with varying degrees of relationship to the corresponding estimation sets revealed that close relatedness leads to a substantial increase in the proportion of total genotypic variance explained by the identified QTL and consequently to an overoptimistic judgment of the precision of marker-assisted selection.