| NON-TECHNICAL SUMMARY: Information on the genes controlling economically-important traits is important for future improvements in crop productivity and quality. Special, designed experiments are typically needed in gene mapping: such experiments permit the study of the relationship between variation in DNA (i.e., genomic data) and variation in the trait of interest (i.e., phenotypic data). On the other hand, public and private breeding programs in major crops accumulate massive amounts of phenotypic data each year. These data that are routinely generated in the course of a breeding program are underutilized in gene mapping. The goal of this research is to develop methods for mapping genes from (1) phenotypic data that are routinely generated in a plant breeding program, (2) pedigree records that are kept in the course of a breeding program, and (3) genomic data that are generated from genomic screens of experimental varieties used in breeding. By computer modeling, the usefulness of this in silico mapping approach will be evaluated in the context of breeding programs for two of the major crops in the U.S., soybean and maize. The usefulness of in silico mapping will be examined under different genetic models. Because this research aims to exploit existing data in mining for genes, a greater leverage of current investments in crop variety development and in plant genome research can be achieved.
OBJECTIVES: The goal of this research is to develop methods for mapping genes from (1) phenotypic data that are routinely generated in a plant breeding program, (2) pedigree records that are kept in the course of a breeding program, and (3) genomic data that are generated from genomic screens of experimental varieties used in breeding.
APPROACH: By computer modeling, the usefulness of an in silico mapping approach, via mixed-model analysis, will be evaluated in the context of breeding programs for two of the major crops in the U.S., soybean (a self-pollinated crop) and maize (a cross-pollinated crop). A trait controlled by 20 non-epistatic quantitative trait nucleotides (QTN) and by 80 QTN that show epistasis in a metabolic pathway will be studied. The analysis will be performed under different levels of trait heritability, numbers of molecular markers, and population sizes.
PROGRESS: 2004/01 TO 2004/12
Most quantitative trait locus (QTL) mapping studies in plants have used designed mapping populations. As an alternative approach, can QTL be detected in silico from phenotypic, genotypic, and pedigree data that are routinely generated in plant breeding programs? We investigated, by computer simulation, the usefulness of in silico mapping via a mixed-model approach in maize, a hybrid species. For stringent significance levels, the power to detect QTL ranged from 0.01 to 0.59. The false discovery rate ranged from 0.05 to 0.74. As with designed mapping experiments, a large sample size, high marker density, high heritability, and small number of QTL led to the highest power for in silico mapping. The power to detect QTL with large effects was greater than the power to detect QTL with small effects. We concluded that gene discovery in hybrid crops can be initiated by in silico mapping. Finding an acceptable compromise, however, between the power to detect QTL and the proportion of false QTL would be necessary. We are currently conducting a parallel study for self-pollinated crops, with soybean as the model species.
IMPACT: 2004/01 TO 2004/12
This research will enable the identification of a whole suite of genes--or higher crop yields, tolerance to environmental stresses, or resistance to insect pests and diseases--without the need for specialized experiments for gene mapping. Economic and environmental benefits would subsequently arise from the routine identification of such genes.
PUBLICATIONS: 2004/01 TO 2004/12
1. Parisseaux, B., and R. Bernardo. 2004. In silico mapping of quantitative trait loci in maize. Theor. Appl. Genet. 109: 508-514.
2. Yu, Y., M. Arbelbide, and R. Bernardo. 2005. Power of in silico mapping of quantitative trait loci via a mixed-model approach in hybrid crops. Theor. Appl. Genet. (accepted for publication).
PROJECT CONTACT:
Name: Bernardo, R. N.
Phone: 612-625-6282
Fax: 612-625-1268
Email: berna022@umn.edu | 
