Improving the Accuracy of Genomic Predictions: Investigation of Training Methods and Data Pooling

One of the primary factors in the response to selection is the accuracy of selection. This study focused on methodologies to predict breeding values (BV) accurately within multi- and single-step genomic evaluations. Factors including cross-validation methods, dependent variables, and genotyping strategies were assessed on the accuracy of genomic BV while using multi-step prediction in real and simulated data. In both cases, random clustering led to largest estimated accuracies compared to clusters based on k-means, k-medoids, and principle component analysis, but differences in bias were not detected. Using deregressed estimated BV (EBV) to estimate SNP effects led to larger accuracies and smaller standard errors than adjusted phenotypes. Randomly genotyping animals instead of selectively genotyping the top 25% was associated with highest accuracies and least amount of bias. Genetic improvement of economically relevant traits (ERT) should be the goal of breeding programs. Although generally absent in seedstock herds, ERT are routinely collected within commercial sectors; therefore, pooling data was proposed to include commercial information in a cost-effective manner. Pooling involves collecting tissue samples from a group of animals and then combining the DNA to be genotyped as one. The accuracy of EBV when pooled data were used within single-step analysis was investigated through simulation. For a single trait, pool sizes of 2, 10, 20 or 50 did not generally lead to differences in EBV accuracy compared to using individual data when pools were constructed to minimize phenotypic variation. Low accuracy sires benefited the most from pooling, while EBV for the pools could be used for management purposes. For a bivariate analysis, pool sizes of at least 20 were recommended in combination with minimizing phenotypic variation. Additionally, if pools were constructed to minimize phenotypic variation, pooling could be used across a range of genetic correlations (0.1, 0.4, and 0.7) and ways in which missing values arise (randomly missing records or sequential culling). Collectively, these results suggest pooling can be used to include commercial data within genetic evaluations. Advisor: Matthew L. Spangle

Improving the Accuracy of Genomic Predictions: Investigation of Training Methods and Data Pooling

One of the primary factors in the response to selection is the accuracy of selection. This study focused on methodologies to predict breeding values (BV) accurately within multi- and single-step genomic evaluations. Factors including cross-validation methods, dependent variables, and genotyping strategies were assessed on the accuracy of genomic BV while using multi-step prediction in real and simulated data. In both cases, random clustering led to largest estimated accuracies compared to clusters based on k-means, k-medoids, and principle component analysis, but differences in bias were not detected. Using deregressed estimated BV (EBV) to estimate SNP effects led to larger accuracies and smaller standard errors than adjusted phenotypes. Randomly genotyping animals instead of selectively genotyping the top 25% was associated with highest accuracies and least amount of bias. Genetic improvement of economically relevant traits (ERT) should be the goal of breeding programs. Although generally absent in seedstock herds, ERT are routinely collected within commercial sectors; therefore, pooling data was proposed to include commercial information in a cost-effective manner. Pooling involves collecting tissue samples from a group of animals and then combining the DNA to be genotyped as one. The accuracy of EBV when pooled data were used within single-step analysis was investigated through simulation. For a single trait, pool sizes of 2, 10, 20 or 50 did not generally lead to differences in EBV accuracy compared to using individual data when pools were constructed to minimize phenotypic variation. Low accuracy sires benefited the most from pooling, while EBV for the pools could be used for management purposes. For a bivariate analysis, pool sizes of at least 20 were recommended in combination with minimizing phenotypic variation. Additionally, if pools were constructed to minimize phenotypic variation, pooling could be used across a range of genetic correlations (0.1, 0.4, and 0.7) and ways in which missing values arise (randomly missing records or sequential culling). Collectively, these results suggest pooling can be used to include commercial data within genetic evaluations. Advisor: Matthew L. Spangle

Comparison of Urban Tree Canopy Classification With High Resolution Satellite Imagery and Three Dimensional Data Derived From LIDAR and Stereoscopic Sensors

Indiana University-Purdue University Indianapolis (IUPUI)Despite growing recognition as a significant natural resource, methods for accurately estimating urban tree canopy cover extent and change over time are not well-established. This study evaluates new methods and data sources for mapping urban tree canopy cover, assessing the potential for increased accuracy by integrating high-resolution satellite imagery and 3D imagery derived from LIDAR and stereoscopic sensors. The results of urban tree canopy classifications derived from imagery, 3D data, and vegetation index data are compared across multiple urban land use types in the City of Indianapolis, Indiana. Results indicate that incorporation of 3D data and vegetation index data with high resolution satellite imagery does not significantly improve overall classification accuracy. Overall classification accuracies range from 88.34% to 89.66%, with resulting overall Kappa statistics ranging from 75.08% to 78.03%, respectively. Statistically significant differences in accuracy occurred only when high resolution satellite imagery was not included in the classification treatment and only the vegetation index data or 3D data were evaluated. Overall classification accuracy for these treatment methods were 78.33% for both treatments, with resulting overall Kappa statistics of 51.36% and 52.59%

Trade effects of regional standards liberalization : a heterogeneous firms approach

This study investigates trade effects of the regional liberalization of technical barriers to trade (TBTs) in the form of harmonization and mutual recognition agreements (MRAs) for testing procedures. The theoretical part of the paper is framed in terms of a heterogeneous firms approach. This paper adds to the existing literature by formalizing the effects of MRAs and harmonization initiatives on bilateral trade flows and by applying this new theoretical framework in the empirical part of the paper. The latter consists of a two-stage gravity estimation and investigates sectoral effects of TBT liberalization on parties to the agreement as well as excluded industrial and developing countries. It finds that MRAs have a strong positive influence on both export probabilities and trade volumes for partner countries. Regarding harmonization, results seem to suggest that the impact on parties to the agreement is negligible, however that on excluded OECD countries is large and positive. Third party developing countries do not seem to benefit from the market integration effect brought about by harmonization in other regions. Overall, effects on the probability that a new firm will export are much more pronounced than effects on the trade volumes of incumbent exporters.Trade and Regional Integration,Economic Theory&Research,Markets and Market Access,Free Trade,Science Education