Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance

QTL "mapping as-you-go"

This invention provides methods for monitoring QTL effects and marker assisted selection (MAS) involving providing a recursively determined correlation between one or more markers and a phenotype of interest

Back to the future: Implications of genetic complexity for hybrid breeding strategies

AbstractCommercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate inbred use reduce effective population size and constrain the accessible genetic space. Under these conditions, long term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study we therefore aim to propose an alternative rational for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this we use simulations based on theNKmodel of genetic architecture. We indeed found that constraining genetic space and reducing effective population size, through program decentralization and disproportionate inbred use, is required to expose additive genetic variation and thus facilitate heritable genetic gains. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for “why things worked” in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.</jats:p

Back to the future: implications of genetic complexity for the structure of hybrid breeding programs

Abstract Commercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate contribution of inbred lines reduce effective population size and constrain the accessible genetic space. Under these conditions, long-term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study, we therefore aim to propose a rationale for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this, we use simulations based on the NK model of genetic architecture. We indeed found that constraining genetic space through program decentralization and disproportionate contribution of parental inbred lines, is required to expose additive genetic variation and thus facilitate heritable genetic gains under high levels of genetic complexity. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for “why things worked” in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.</jats:p

Modeling QTL effects and MAS in plant breeding

The empirical evidence accumulated to date indicates that the genetic architecture of the different traits of organisms, emphasizing here those relevant to plant breeding, should be viewed as a genetic complexity continuum. This concept is not new to plant breeders. What is new is that geneticists and plant breeders can now apply high throughput molecular technologies to identify and study the genes and alleles responsible for the standing genetic and phenotypic variation for traits in elite breeding populations. Plant breeders undertake research to develop robust breeding strategies that take advantage of this growing body of trait genetics knowledge and seek breeding methods that can be practically applied to improve multiple traits to achieve defined breeding objectives. While experimental and quantitative methods are developed to detect quantitative trait loci (QTL) and to implement marker-assisted selection (MAS) for the detected trait QTL as components of a comprehensive plant breeding strategy, simulation modeling methods can be applied to quantify the robustness of the chosen QTL analysis and MAS methods for the trait genetics complexity continuum. We review methods that can be applied to model the effects of QTL and outcomes from MAS in plant breeding as our view of the trait genetic complexity continuum unfolds. Some key lessons from this body of research are discussed

Using clusters of computers for large QU-GENE simulation experiments

The QU-GENE Computing Cluster (QCC) is a hardware and software solution to the automation and speedup of large QU-GENE (QUantitative GENEtics) simulation experiments that are designed to examine the properties of genetic models, particularly those that involve factorial combinations of treatment levels. QCC automates the management of the distribution of components of the simulation experiments among the networked single-processor computers to achieve the speedup

Mapping as you go: an effective approach for marker-assisted selection of complex traits

The advent of high throughput molecular technologies has led to an expectation that breeding programs will use marker-trait associations to conduct marker-assisted selection (MAS) for traits. Many challenges exist with this molecular breeding approach for so-called complex traits. A major restriction to date has been the limited ability to detect and quantify marker-trait relationships, especially for traits influenced by the effects of gene-by-gene and gene-by-environment interactions. A further complication has been that estimates of quantitative trait loci (QTL) effects are biased by the necessity of working with a limited set of genotypes in a limited set of environments, and hence the applications of these estimates are not as effective as expected when used more broadly within a breeding program. The approach considered in this paper, referred to as the Mapping As You Go (MAYG) approach, continually revises estimates of QTL allele effects by remapping new elite germplasm generated over cycles of selection, thus ensuring that QTL estimates remain relevant to the current set of germplasm in the breeding program. Mapping As You Go is a mapping-MAS strategy that explicitly recognizes that alleles of QTL for complex traits can have different values as the current breeding material changes with time. Simulation was used to investigate the effectiveness of the MAYG approach applied to complex traits. The results indicated that greater levels of response were achieved and these responses were less variable when estimates were revised frequently compared with situations where estimates were revised infrequently or not at all

Gene-to-phenotype models and complex trait genetics

The premise that is explored in this paper is that in some cases, in order to make progress in the design of molecular breeding strategies for complex traits, we will need a theoretical framework for quantitative genetics that is grounded in the concept of gene-networks. We seek to develop a gene-to-phenotype (G --> P) modelling framework for quantitative genetics that explicitly deals with the context-dependent gene effects that are attributed to genes functioning within networks, i.e. epistasis, gene x environment interactions, and pleiotropy. The E(NK) model is discussed as a starting point for building such a theoretical framework for complex trait genetics. Applying this framework to a combination of theoretical and empirical G --> P models, we find that although many of the context-dependent effects of genetic variation on phenotypic variation can reduce the rate of genetic progress from breeding, it is possible to design molecular breeding strategies for complex traits that on average will outperform phenotypic selection. However, to realise these potential advantages, empirical G --> P models of the traits will need to take into consideration the context-dependent effects that are a consequence of epistasis, gene x environment interactions, and pleiotropy. Some promising G --> P modelling directions are discussed