Sensitivity Analysis and Uncertainty Analysis in Gene-Based Modeling. (3415)
Authors:
J.W. White* - USDA-ARS, Phoenix G. Hoogenboom - Univ. Georgia, Griffin C. Messina - Univ. Florida
Abstract:
Incorporating explicit genetic information into crop models creates important opportunities for sensitivity and uncertainty analyses. Characterizing cultivars through effects of specific genetic loci offers several advantages over conventional cultivar-specific parameters such as “genetic coefficients.” Sensitivity analysis can focus on the known range of genetic variation by sampling specific gene
combinations. Assuming pleiotropic effects are correctly described, sampling gene combinations should ensure that traits such as leaf size, leaf thickness, and potential photosynthetic rate are varied such that inherent physiological or developmental interrelations are better reflected. Gene-based approaches also can reduce model uncertainty since genotypes can be determined more precisely than with phenotypic data traditionally used to calibrate cultivar responses. We illustrate sensitivity and uncertainty analyses for scenarios related to global warming, considering the GeneGro model and multiple loci of common bean. For pure (homozygous) lines with two alleles per locus, possible gene combinations increase as 2n, where n is the number of loci, but considering epistatic effects and undesirable gene combinations (e.g., for too-small grain size) can reduce this number. The advantages outlined above argue strongly for wider use of gene-based modeling.
Speaker Information: Jeffrey White, USDA-ARS, Phoenix, 4331 E Broadway Rd., Phoenix, AZ 85020; Phone: 602-437-1702 x 268; E-mail: [email protected]
Session Information: Monday, November 1, 2004, 7:55 AM-3:00 PM Presentation Start: 1:15 PM
Keywords: modeling; genomics; sensitivity analysis; wheat