{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132795"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132795","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"The role of genomic selection and advanced statistical modeling in domesticating perennial sorghum","abstract":"The perennialization of agronomically valuable grain crops, such as sorghum (Sorghum bicolor (L.)), has the potential to significantly enhance sustainable agriculture and food security, particularly in regions experiencing soil degradation, water scarcity, and resource limitations. Genomic prediction (GP) is a crucial tool for accelerating the selection process in breeding perennial sorghum for key domestication and perenniality traits. The goal of the research presented in this dissertation is to assess and implement the most effective GP model for accelerating breeding cycles. This work focused on two key questions. First, are there benefits of incorporating genetic architecture (using multi-kernel) into GP predictive ability? Second, can accounting for Genotype × Environment (GxE) interactions, including the use of environmental covariates (ECs), improve predictive ability? We found that partitioning single nucleotide polymorphisms (SNPs) based on a priori genetic features did not consistently boost predictive ability when compared to standard genomic best linear unbiased prediction (GBLUP) for these populations. Therefore, we inferred that the added complexity of MultiBLUP is not worth the negligible gains. In our multi-environment studies, we found that simple traits such as flowering time showed high and stable predictive ability. In contrast, perenniality traits were more complex to predict, and including ECs had little effect on improving their predictive ability. We developed GenSelect, an accessible R Shiny application that implements advanced GP models (GBLUP, MultiBLUP, GxE with and without ECs), allowing practitioners to easily upload their own data and identify the optimal model for their unique accessions. This application makes these models usable and broadens the potential impact of this research. This dissertation aims to identify the most effective and practical genomic prediction strategies for accelerating the breeding of perennial sorghum. These findings and the GenSelect tool facilitate the development of a practical GP pipeline for perennialized sorghum, guiding future efforts to create environmentally sustainable crops and enhance food security. Future research should focus on obtaining below-ground environmental covariates and collecting data in more diverse environments to boost multi-environment predictive abilities for perenniality traits.","abstract_html":"The perennialization of agronomically valuable grain crops, such as sorghum (Sorghum bicolor (L.)), has the potential to significantly enhance sustainable agriculture and food security, particularly in regions experiencing soil degradation, water scarcity, and resource limitations. Genomic prediction (GP) is a crucial tool for accelerating the selection process in breeding perennial sorghum for key domestication and perenniality traits. The goal of the research presented in this dissertation is to assess and implement the most effective GP model for accelerating breeding cycles. This work focused on two key questions. First, are there benefits of incorporating genetic architecture (using multi-kernel) into GP predictive ability? Second, can accounting for Genotype × Environment (GxE) interactions, including the use of environmental covariates (ECs), improve predictive ability? We found that partitioning single nucleotide polymorphisms (SNPs) based on a priori genetic features did not consistently boost predictive ability when compared to standard genomic best linear unbiased prediction (GBLUP) for these populations. Therefore, we inferred that the added complexity of MultiBLUP is not worth the negligible gains. In our multi-environment studies, we found that simple traits such as flowering time showed high and stable predictive ability. In contrast, perenniality traits were more complex to predict, and including ECs had little effect on improving their predictive ability. We developed GenSelect, an accessible R Shiny application that implements advanced GP models (GBLUP, MultiBLUP, GxE with and without ECs), allowing practitioners to easily upload their own data and identify the optimal model for their unique accessions. This application makes these models usable and broadens the potential impact of this research. This dissertation aims to identify the most effective and practical genomic prediction strategies for accelerating the breeding of perennial sorghum. These findings and the GenSelect tool facilitate the development of a practical GP pipeline for perennialized sorghum, guiding future efforts to create environmentally sustainable crops and enhance food security. Future research should focus on obtaining below-ground environmental covariates and collecting data in more diverse environments to boost multi-environment predictive abilities for perenniality traits.","abstract_has_math":false,"creators":["Widener, Sarah Joy Rhodes RIchards"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Crop Sciences","degree_department":null,"school":null,"contributors":["Lipka, Alexander E","Jarquin, Diego","Sacks, Erik","Jamann, Tiffany M"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Perennial Sorghum","Sorghum","Genomic Prediction","Genotype by Environment Interactions","Genomic Best Linear Unbiased Prediction","MultiBLUP","Environmental Covariates","Food Security","Sustainable Agriculture","GenSelect"],"languages":["en"],"rights":["Copyright 2025 Sarah Widener"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132795","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lipka, Alexander E","Jarquin, Diego","Sacks, Erik","Jamann, Tiffany M"]},{"key":"dc:creator","label":"Author","values":["Widener, Sarah Joy Rhodes RIchards"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Crop Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Perennial Sorghum","Sorghum","Genomic Prediction","Genotype by Environment Interactions","Genomic Best Linear Unbiased Prediction","MultiBLUP","Environmental Covariates","Food Security","Sustainable Agriculture","GenSelect"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Sarah Widener"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132795"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The perennialization of agronomically valuable grain crops, such as sorghum (Sorghum bicolor (L.)), has the potential to significantly enhance sustainable agriculture and food security, particularly in regions experiencing soil degradation, water scarcity, and resource limitations. Genomic prediction (GP) is a crucial tool for accelerating the selection process in breeding perennial sorghum for key domestication and perenniality traits. The goal of the research presented in this dissertation is to assess and implement the most effective GP model for accelerating breeding cycles. This work focused on two key questions. First, are there benefits of incorporating genetic architecture (using multi-kernel) into GP predictive ability? Second, can accounting for Genotype × Environment (GxE) interactions, including the use of environmental covariates (ECs), improve predictive ability? We found that partitioning single nucleotide polymorphisms (SNPs) based on a priori genetic features did not consistently boost predictive ability when compared to standard genomic best linear unbiased prediction (GBLUP) for these populations. Therefore, we inferred that the added complexity of MultiBLUP is not worth the negligible gains. In our multi-environment studies, we found that simple traits such as flowering time showed high and stable predictive ability. In contrast, perenniality traits were more complex to predict, and including ECs had little effect on improving their predictive ability. We developed GenSelect, an accessible R Shiny application that implements advanced GP models (GBLUP, MultiBLUP, GxE with and without ECs), allowing practitioners to easily upload their own data and identify the optimal model for their unique accessions. This application makes these models usable and broadens the potential impact of this research. This dissertation aims to identify the most effective and practical genomic prediction strategies for accelerating the breeding of perennial sorghum. These findings and the GenSelect tool facilitate the development of a practical GP pipeline for perennialized sorghum, guiding future efforts to create environmentally sustainable crops and enhance food security. Future research should focus on obtaining below-ground environmental covariates and collecting data in more diverse environments to boost multi-environment predictive abilities for perenniality traits.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-12-01","The student, Sarah Widener, accepted the attached license on 2025-12-03 at 16:50.","The student, Sarah Widener, submitted this Dissertation for approval on 2025-12-03 at 16:57.","This Dissertation was approved for publication on 2025-12-05 at 08:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23051 on 2026-02-19 at 20:09:55"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["The role of genomic selection and advanced statistical modeling in domesticating perennial sorghum"]}]}],"canonical_facts":{"dc:contributor":["Lipka, Alexander E","Jarquin, Diego","Sacks, Erik","Jamann, Tiffany M"],"dc:creator":["Widener, Sarah Joy Rhodes RIchards"],"dc:date":["2025-12","2025-12-05"],"dc:description":["The perennialization of agronomically valuable grain crops, such as sorghum (Sorghum bicolor (L.)), has the potential to significantly enhance sustainable agriculture and food security, particularly in regions experiencing soil degradation, water scarcity, and resource limitations. Genomic prediction (GP) is a crucial tool for accelerating the selection process in breeding perennial sorghum for key domestication and perenniality traits. The goal of the research presented in this dissertation is to assess and implement the most effective GP model for accelerating breeding cycles. This work focused on two key questions. First, are there benefits of incorporating genetic architecture (using multi-kernel) into GP predictive ability? Second, can accounting for Genotype × Environment (GxE) interactions, including the use of environmental covariates (ECs), improve predictive ability? We found that partitioning single nucleotide polymorphisms (SNPs) based on a priori genetic features did not consistently boost predictive ability when compared to standard genomic best linear unbiased prediction (GBLUP) for these populations. Therefore, we inferred that the added complexity of MultiBLUP is not worth the negligible gains. In our multi-environment studies, we found that simple traits such as flowering time showed high and stable predictive ability. In contrast, perenniality traits were more complex to predict, and including ECs had little effect on improving their predictive ability. We developed GenSelect, an accessible R Shiny application that implements advanced GP models (GBLUP, MultiBLUP, GxE with and without ECs), allowing practitioners to easily upload their own data and identify the optimal model for their unique accessions. This application makes these models usable and broadens the potential impact of this research. This dissertation aims to identify the most effective and practical genomic prediction strategies for accelerating the breeding of perennial sorghum. These findings and the GenSelect tool facilitate the development of a practical GP pipeline for perennialized sorghum, guiding future efforts to create environmentally sustainable crops and enhance food security. Future research should focus on obtaining below-ground environmental covariates and collecting data in more diverse environments to boost multi-environment predictive abilities for perenniality traits.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-12-01","The student, Sarah Widener, accepted the attached license on 2025-12-03 at 16:50.","The student, Sarah Widener, submitted this Dissertation for approval on 2025-12-03 at 16:57.","This Dissertation was approved for publication on 2025-12-05 at 08:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23051 on 2026-02-19 at 20:09:55"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132795"],"dc:language":["en"],"dc:rights":["Copyright 2025 Sarah Widener"],"dc:subject":["Perennial Sorghum","Sorghum","Genomic Prediction","Genotype by Environment Interactions","Genomic Best Linear Unbiased Prediction","MultiBLUP","Environmental Covariates","Food Security","Sustainable Agriculture","GenSelect"],"dc:title":["The role of genomic selection and advanced statistical modeling in domesticating perennial sorghum"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Crop Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}