SummaryUnderstanding and estimating the structure and parameters associated with the genetic architecture of quantitative traits is a major research focus in quantitative genetics. With the availability of a well-saturated genetic map of molecular markers, it is possible to identify a major part of the structure of the genetic architecture of quantitative traits and to estimate the associated parameters. Multiple interval mapping, which was recently proposed for simultaneously mapping multiple quantitative trait loci (QTL), is well suited to the identification and estimation of the genetic architecture parameters, including the number, genomic positions, effects and interactions of significant QTL and their contribution to the genetic variance. With multiple traits and multiple environments involved in a QTL mapping experiment, pleiotropic effects and QTL by environment interactions can also be estimated. We review the method and discuss issues associated with multiple interval mapping, such as likelihood analysis, model selection, stopping rules and parameter estimation. The potential power and advantages of the method for mapping multiple QTL and estimating the genetic architecture are discussed. We also point out potential problems and difficulties in resolving the details of the genetic architecture as well as other areas that require further investigation. One application of the analysis is to improve genome-wide marker-assisted selection, particularly when the information about epistasis is used for selection with mating.
Evolutionary biologists seek to understand the genetic basis for multivariate phenotypic divergence. We constructed an F 2 mapping population (N ¼ 539) between two distinct populations of Mimulus guttatus. We measured 20 floral, vegetative, and life-history characters on parents and F 1 and F 2 hybrids in a common garden experiment. We employed multitrait composite interval mapping to determine the number, effect, and degree of pleiotropy in quantitative trait loci (QTL) affecting divergence in floral, vegetative, and lifehistory characters. We detected 16 QTL affecting floral traits; 7 affecting vegetative traits; and 5 affecting selected floral, vegetative, and life-history traits. Floral and vegetative traits are clearly polygenic. We detected a few major QTL, with all remaining QTL of small effect. Most detected QTL are pleiotropic, implying that the evolutionary shift between these annual and perennial populations is constrained. We also compared the genetic architecture controlling floral trait divergence both within (our intraspecific study) and between species, on the basis of a previously published analysis of M. guttatus and M. nasutus. Eleven of our 16 floral QTL map to approximately the same location in the interspecific map based on shared, collinear markers, implying that there may be a shared genetic basis for floral divergence within and among species of Mimulus.
Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize. Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5% of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65% improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.
Animal-mediated pollination is essential in the reproductive biology of many flowering plants and tends to be associated with pollination syndromes, sets of floral traits that are adapted to particular groups of pollinators. The complexity and functional convergence of various traits within pollination syndromes are outstanding examples of biological adaptation, raising questions about their mechanisms and origins. In the genus Petunia, complex pollination syndromes are found for nocturnal hawkmoths (P. axillaris) and diurnal bees (P. integrifolia), with characteristic differences in petal color, corolla shape, reproductive organ morphology, nectar quantity, nectar quality, and fragrance. We dissected the Petunia syndromes into their most important phenotypic and genetic components. They appear to include several distinct differences, such as cell-growth and cell-division patterns in the basal third of the petals, elongation of the ventral stamens, nectar secretion and nectar sugar metabolism, and enzymatic differentiation in the phenylpropanoid pathway. In backcross-inbred lines of species-derived chromosome segments in a transposon tagging strain of P. hybrida, one to five quantitative trait loci were identified for each syndrome component. Two loci for stamen elongation and nectar volume were confirmed in introgression lines and showed large allelic differences. The combined data provide a framework for a detailed understanding of floral syndromes from their developmental and molecular basis to their impact on animal behavior. With its molecular genetic tools, this Petunia system provides a novel venue for a pattern of adaptive radiation that is among the most characteristic of flowering plants. SEXUAL reproduction in flowering plants is depennation by birds, bats, butterflies, or flies, each with distinct characteristics (Proctor et al. 1996). Pollination dent on the correct delivery of pollen grains to conspecific stigmata. In nature, the vectors for this delivery syndromes are outstanding examples of biological adaptation, involving a variety of developmental and physioare often animals that forage for pollen and nectar. Many flowers have evolved complex syndromes of morlogical systems. They raise questions about their genetic basis and the mutational steps that lead from one polliphological and physiological characters to attract and nation syndrome to another. reward pollinators and thereby to enhance the effiDifferent pollination syndromes can often be seen ciency and accuracy of pollen deposition (e.g., Proctor among closely related and sexually compatible sister et al. 1996). Pollinators are important factors for the species, enabling their genetic dissection. For example, evolution of variety in floral display among angiosperms interspecific crosses of bird-and bee-pollinated species and in some cases can contribute to reproductive isolaof the genus Mimulus (Bradshaw et al. 1995; Bradtion by differential visitation (e.g., Grant 1994).shaw et al. 1998) have suggested a relatively simple In m...
Predictive ability derived from gene expression and metabolic information was evaluated using genomic prediction methods based on datasets from a public maize panel. With the rapid development of high throughput biological technologies, information from gene expression and metabolites has received growing attention in plant genetics and breeding. In this study, we evaluated the utility of gene expression and metabolic information for genomic prediction using data obtained from a maize diversity panel. Our results show that, when used as predictor variables, gene expression levels and metabolite abundances provided reasonable predictive abilities relative to those based on genetic markers, although these values were not as large as those with genetic markers. Integrating gene expression levels and metabolite abundances with genetic markers significantly improved predictive abilities in comparison to the benchmark genomic best linear unbiased prediction model using genome-wide markers only. Predictive abilities based on gene expression and metabolites were trait-specific and were affected by the time of measurement and tissue samples as well as the number of genes and metabolites included in the model. In general, our results suggest that, rather than being conventionally used as intermediate phenotypes, gene expression and metabolic information can be used as predictors for genomic prediction and help improve genetic gains for complex traits in breeding programs.
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