Ecology meets molecular genetics in Arabidopsis

 Arabidopsis thaliana has been used as a model plant for molecular genetics studies. Recently, Arabidopsis has been utilized in an increasing number of ecological and evolutionary studies. These studies are beginning to explain three “black boxes” of ecology: the nature of the mutations responsible for phenotypic variation, female–male interactions in the pistil, and the genetic basis of speciation. Among the advances are the inclusive fitness of haploid gametophytes, the testing of the arms-race model, morphological diversity, and reproductive isolation. Important methods include quantitative trait locus (QTL) mapping and molecular population genetics. Most significantly, natural variations of various aspects are now available via the taxonomic revision of its closely related species, and by the worldwide collection of accessions. Some conspicuous works using maize, Drosophila, and other species will be also discussed. Received: June 1, 2002 / Accepted: October 15, 2002 Acknowledgments I would like to express my deepest gratitude to Prof. K. Okada for supporting this research. I thank the organizers of the symposium for fruitful discussion, T. Yahara and M. Kanaoka for critical reading, T. Araki and T. Kenta for valuable discussions, and members of the Okada laboratory for technical assistance and discussion. The work was supported by JSPS Research Fellowships for Young Scientists.     

From plant genomics to breeding practice

New alleles are constantly accumulated during intentional crop selection. The molecular understanding of these alleles has stimulated new genomic approaches to mapping quantitative trait loci (QTL) and haplotype multiplicity of the genes concerned. A limited number of quantitative trait nucleotides responsible for QTL variation have been described, but an acceleration in their rate of discovery is expected with the adoption of linkage disequilibrium and candidate gene strategies for QTL fine mapping and cloning. Additional layers of regulatory variation have been studied that could also contribute to the molecular basis of quantitative genetics of crop traits. Despite this progress, the role of marker-assisted selection in plant breeding will ultimately depend on the genetic model underlying quantitative variation.     

Efficiency of selective genotyping for genetic analysis of complex traits and potential applications in crop improvement

Selective genotyping of individuals from the two tails of the phenotypic distribution of a population provides a cost efficient alternative to analysis of the entire population for genetic mapping. Past applications of this approach have been confounded by the small size of entire and tail populations, and insufficient marker density, which result in a high probability of false positives in the detection of quantitative trait loci (QTL). We studied the effect of these factors on the power of QTL detection by simulation of mapping experiments using population sizes of up to 3,000 individuals and tail population sizes of various proportions, and marker densities up to one marker per centiMorgan using complex genetic models including QTL linkage and epistasis. The results indicate that QTL mapping based on selective genotyping is more powerful than simple interval mapping but less powerful than inclusive composite interval mapping. Selective genotyping can be used, along with pooled DNA analysis, to replace genotyping the entire population, for mapping QTL with relatively small effects, as well as linked and interacting QTL. Using diverse germplasm including all available genetics and breeding materials, it is theoretically possible to develop an “all-in-one plate” approach where one 384-well plate could be designed to map almost all agronomic traits of importance in a crop species. Selective genotyping can also be used for genomewide association mapping where it can be integrated with selective phenotyping approaches. We also propose a breeding-to-genetics approach, which starts with identification of extreme phenotypes from segregating populations generated from multiple parental lines and is followed by rapid discovery of individual genes and combinations of gene effects together with simultaneous manipulation in breeding programs.     

Advanced backcross-QTL analysis in spring barley (H. vulgare ssp. spontaneum) comparing a REML versus a Bayesian model in multi-environmental field trials

A common difficulty in mapping quantitative trait loci (QTLs) is that QTL effects may show environment specificity and thus differ across environments. Furthermore, quantitative traits are likely to be influenced by multiple QTLs or genes having different effect sizes. There is currently a need for efficient mapping strategies to account for both multiple QTLs and marker-by-environment interactions. Thus, the objective of our study was to develop a Bayesian multi-locus multi-environmental method of QTL analysis. This strategy is compared to (1) Bayesian multi-locus mapping, where each environment is analysed separately, (2) Restricted Maximum Likelihood (REML) single-locus method using a mixed hierarchical model, and (3) REML forward selection applying a mixed hierarchical model. For this study, we used data on multi-environmental field trials of 301 BC₂DH lines derived from a cross between the spring barley elite cultivar Scarlett and the wild donor ISR42-8 from Israel. The lines were genotyped by 98 SSR markers and measured for the agronomic traits “ears per m2,” “days until heading,” “plant height,” “thousand grain weight,” and “grain yield”. Additionally, a simulation study was performed to verify the QTL results obtained in the spring barley population. In general, the results of Bayesian QTL mapping are in accordance with REML methods. In this study, Bayesian multi-locus multi-environmental analysis is a valuable method that is particularly suitable if lines are cultivated in multi-environmental field trials. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Systems genetics in “-omics” era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP–SNP genetic interactions, LD block–block interactions, miRNA–mRNA interactions, DNA–protein interactions, protein–protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate “-omics” datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.     

Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits

A central challenge in evolutionary biology is to identify genes underlying ecologically important traits and describe the fitness consequences of naturally occurring variation at these loci. To address this goal, several novel approaches have been developed, including 'population genomics,' where a large number of molecular markers are scored in individuals from different environments with the goal of identifying markers showing unusual patterns of variation, potentially due to selection at linked sites. Such approaches are appealing because of (1) the increasing ease of generating large numbers of genetic markers, (2) the ability to scan the genome without measuring phenotypes and (3) the simplicity of sampling individuals without knowledge of their breeding history. Although such approaches are inherently applicable to non-model systems, to date these studies have been limited in their ability to uncover functionally relevant genes. By contrast, quantitative genetics has a rich history, and more recently, quantitative trait locus (QTL) mapping has had some success in identifying genes underlying ecologically relevant variation even in novel systems. QTL mapping, however, requires (1) genetic markers that specifically differentiate parental forms, (2) a focus on a particular measurable phenotype and (3) controlled breeding and maintenance of large numbers of progeny. Here we present current advances and suggest future directions that take advantage of population genomics and quantitative genetic approaches - in both model and non-model systems. Specifically, we discuss advantages and limitations of each method and argue that a combination of the two provides a powerful approach to uncovering the molecular mechanisms responsible for adaptation.     

Inheritance of Flowering Time in Apricot (<Emphasis Type="Italic">Prunus armeniaca</Emphasis> L.) and Analysis of Linked Quantitative Trait Loci (QTLs) using Simple Sequence Repeat (SSR) Markers

Time of flowering was studied during 3 years in a BC1 apricot progeny of 73 seedlings derived from a cross between the F1 selection “Z506-07” (“Orange Red” × “Currot”) and the Spanish cultivar “Currot”. Results indicated a quantitative inheritance of flowering time in apricot with an influence of juvenility and environmental conditions (chill accumulation) on the evaluation and expression of this trait. Genetic maps consisting of 11 linkage groups for both parents representing the eight chromosomes of apricot were developed using 46 apricot and peach simple sequence repeat (SSR-microsatellites) markers and were used for the identification of quantitative trait loci (QTL). QTL analysis for flowering time allowed the identification of one significant QTL on the linkage group 5 (G5) of “Z506-07”, and explaining most of the phenotypic variation. Two microsatellite loci (UDAp-423r and AMPA-105) were found to be tightly linked to this important agronomic trait. Finally, we discuss the stability of the QTL described during the 3 years of the study and the development of efficient marker-assisted selection strategies applied to apricot and other Prunus breeding programs.     

Degeneracy-Driven Self-Structuring Dynamics in Selective Repertoires

Numerous biological interactions, such as interactions between T cell receptors or antibodies with antigens, interactions between enzymes and substrates, or interactions between predators and prey are often not strictly specific. In such less specific, or “sloppy,” systems, referred to here as degenerate systems, a given unit of a diverse resource (antigens, enzymatic substrates, prey) is at risk of being recognized and consumed by multiple consumers (lymphocytes, enzymes, predators). In this study, we model generalized degenerate consumer-resource systems of Lotka–Volterra and Verhulst types. In the degenerate systems of Lotka–Volterra, there is a continuum of types of consumer and resource based on variation of a single trait (characteristic, or preference). The consumers experience competition for a continuum of resource types. This non-local interaction system is modeled with partial differential-integral equations and shows spontaneous self-structuring of the consumer population that depends on the degree of interaction degeneracy between resource and consumer, but does not mirror the distribution of resource. We also show that the classical Verhulst (i.e. logistic) single population model can be generalized to a degenerate model, which shows qualitative behavior similar to that in the degenerate Lotka–Volterra model. These results provide better insight into the dynamics of selective systems in biology, suggesting that adaptation of degenerate repertoires is not a simple “mirroring” of the environment by the “fittest” elements of population.     

Functional genomics complements quantitative genetics in identifying disease-gene associations

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype.     

Network quantitative trait loci mapping of circadian clock outputs identifies metabolic pathway-to-clock linkages in Arabidopsis

Modern systems biology permits the study of complex networks, such as circadian clocks, and the use of complex methodologies, such as quantitative genetics. However, it is difficult to combine these approaches due to factorial expansion in experiments when networks are examined using complex methods. We developed a genomic quantitative genetic approach to overcome this problem, allowing us to examine the function(s) of the plant circadian clock in different populations derived from natural accessions. Using existing microarray data, we defined 24 circadian time phase groups (i.e., groups of genes with peak phases of expression at particular times of day). These groups were used to examine natural variation in circadian clock function using existing single time point microarray experiments from a recombinant inbred line population. We identified naturally variable loci that altered circadian clock outputs and linked these circadian quantitative trait loci to preexisting metabolomics quantitative trait loci, thereby identifying possible links between clock function and metabolism. Using single-gene isogenic lines, we found that circadian clock output was altered by natural variation in Arabidopsis thaliana secondary metabolism. Specifically, genetic manipulation of a secondary metabolic enzyme led to altered free-running rhythms. This represents a unique and valuable approach to the study of complex networks using quantitative genetics.     

Panpsychic Organicism: Sewall Wright’s Philosophy for Understanding Complex Genetic Systems

Sewall Wright first encountered the complex systems characteristic of gene combinations while a graduate student at Harvard’s Bussey Institute from 1912 to 1915. In Mendelian breeding experiments, Wright observed a hierarchical dependence of the organism’s phenotype on dynamic networks of genetic interaction and organization. An animal’s physical traits, and thus its autonomy from surrounding environmental constraints, depended greatly on how genes behaved in certain combinations. Wright recognized that while genes are the material determinants of the animal phenotype, operating with great regularity, the special nature of genetic systems contributes to the animal phenotype a degree of spontaneity and novelty, creating unpredictable trait variations by virtue of gene interactions. As a result of his experimentation, as well as his keen interest in the philosophical literature of his day, Wright was inspired to see genetic systems as conscious, living organisms in their own right. Moreover, he decided that since genetic systems maintain ordered stability and cause unpredictable novelty in their organic wholes (the animal phenotype), it would be necessary for biologists to integrate techniques for studying causally ordered phenomena (experimental method) and chance phenomena (correlation method). From 1914 to 1921 Wright developed his “method of path coefficient” (or “path analysis”), a new procedure drawing from both laboratory experimentation and statistical correlation in order to analyze the relative influence of specific genetic interactions on phenotype variation. In this paper I aim to show how Wright’s philosophy for understanding complex genetic systems (panpsychic organicism) logically motivated his 1914–1921 design of path analysis.     

Genetic Relationships Among Panicle Characteristics of Rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) Using Unconditional and Conditional QTL Analyses

Using mixed-model-based composite interval mapping and conditional statistical methods, we studied quantitative trait loci (QTLs) with epistatic effects and QTLs by environment interaction effects for rice seed set percent (SSP), filled grain number per plant (FGP), and panicle length (PL). A population of 241 recombinant inbred lines was used which was derived from a cross between “Zhenshan 97” and “Minghui 63.” Its linkage map included 221 molecular markers. Our QTL analysis detected 28, 25, and 32 QTLs for SSP, FGP, and PL, respectively. Each QTL explained 1.37%∼13.19% of the mean phenotypic variation. A comparison of conventional and conditional mapping provided information about the genetic control system involved in the synthetic process of SSP, FGP, and PL at the level of individual QTLs. Conditional QTLs with reduced (or increased) effects were identified for SSP, which were significantly influenced by FGP or PL. Some QTLs could express independently for the given traits, thereby providing possibilities for simultaneous improvement of SSR and PL, and SSR and FGP. Epistasis was more sensitive to environmental conditions than were additive effects.