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Full-Text Articles in Life Sciences
Incorporating Sex Chromosomes In Transcriptome Prediction Models And Improving Cross-Population Prediction Performance, Daniel S. Araujo
Incorporating Sex Chromosomes In Transcriptome Prediction Models And Improving Cross-Population Prediction Performance, Daniel S. Araujo
Master's Theses
Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized multivariate adaptive shrinkage may improve cross-population transcriptome prediction, as it leverages effect size estimates across different conditions - in this case, different populations. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWAS) using different methods (Elastic Net, Matrix eQTL and Multivariate Adaptive Shrinkage in R (MASHR)) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in …
Optimizing Gene Expression Prediction And Omics Integration In Populations Of African Ancestry, Paul Chukwuebuka Okoro
Optimizing Gene Expression Prediction And Omics Integration In Populations Of African Ancestry, Paul Chukwuebuka Okoro
Master's Theses
Popular transcriptome imputation methods such as PrediXcan and FUSIon use parametric linear assumptions, and thus are unable to flexibly model the complex genetic architecture of the transcriptome. Although non-linear modeling has been shown to improve imputation performance, replicability and potential cross-population differences have not been adequately studied. Therefore, to optimize imputation performance across global populations, we used the non-linear machine learning (ML) models random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN) to build transcriptome imputation models, and evaluated their performance in comparison to elastic net (EN). We trained gene expression prediction models using genotype and blood …
Root Kinematics In Relation To Temperature And Genome Size In Wild And Domesticated Zea., Avery B. Cromwell
Root Kinematics In Relation To Temperature And Genome Size In Wild And Domesticated Zea., Avery B. Cromwell
Master's Theses
We studied root kinematics in relation to temperature and genome size variation in teosinte (Zea mays subspecies parviglumis) and corn (Zea mays subspecies mays). Corn had significantly faster radicle growth than teosinte when grown at a constant temperature. Both species exhibited variation in seed size and for each species larger seeds had faster root growth. Genome size was not significantly correlated with faster radicle growth rates across multiple land races of corn. To examine temperature dependent growth in corn and teosinte, a germinated seedling was grown at multiple temperatures. Growth rates at these temperatures were used …
Viability Of Alternative Genetic Improvement Strategies Using Whole Genome Selection On Commercial Dairy Operations, Levi W.M. Gassaway
Viability Of Alternative Genetic Improvement Strategies Using Whole Genome Selection On Commercial Dairy Operations, Levi W.M. Gassaway
Master's Theses
The objective of this thesis was to determine the viability of alternative genetic improvement strategies (GIS). Each alternative GIS combined the use of whole genome selection (WGS) with common reproductive methods (non-sexed semen artificial insemination (AI), sexed semen AI, embryo transfer utilizing non-sexed semen AI) that can be found on a commercial dairy operation. The viability of each GIS was determined using a discounted gene flow model, designed with parameters of a typical western dairy operation, to evaluate the following variables: reproductive method, selection intensity, accuracy of prediction and female age-class. Of the GIS investigated, a heifer-based strategy that used …