{"id":{"repo_id":"sdstate","oai_identifier":"oai:openprairie.sdstate.edu:etd-2591"},"canonical_url":"https://search.dev.ndltd.org/etd/sdstate/oai:openprairie.sdstate.edu:etd-2591","repository":{"repo_id":"sdstate","name":"South Dakota State University","base_url":"https://openprairie.sdstate.edu/do/oai/"},"display":{"title":"Genetic Association Mapping : Missing Markers, Epistatic Effects, and Applications","abstract":"<p>Association mapping has been widely used to detect desirable genetic markers associated with traits of interest for plant improvement. Missing marker data are a common and yet challenging issue in many association mapping studies, especially as the number of markers used for these studies is large. On the other hand, selection of several sets of DNA markers with potential epistasis associated with target traits will greatly help plant improvement via a marker assisted selection approach. In this study, we first proposed a linkage-based imputation method for missing marker data given available linkage information and then integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. The simulation studies showed that both the proposed linkage-based imputation method and MDR-based forward selection method performed well. We also applied these methods to determine SNP (single nucleotide polymorphism) markers with potential epistasis associated with agronomic traits in two crops: barley and wheat.</p>","abstract_html":"&lt;p&gt;Association mapping has been widely used to detect desirable genetic markers associated with traits of interest for plant improvement. Missing marker data are a common and yet challenging issue in many association mapping studies, especially as the number of markers used for these studies is large. On the other hand, selection of several sets of DNA markers with potential epistasis associated with target traits will greatly help plant improvement via a marker assisted selection approach. In this study, we first proposed a linkage-based imputation method for missing marker data given available linkage information and then integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. The simulation studies showed that both the proposed linkage-based imputation method and MDR-based forward selection method performed well. We also applied these methods to determine SNP (single nucleotide polymorphism) markers with potential epistasis associated with agronomic traits in two crops: barley and wheat.&lt;/p&gt;","abstract_has_math":false,"creators":["Xu, Yi"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis - University Access Only","degree_discipline":"Plant Science","degree_department":null,"school":null,"contributors":["Jixiang Wu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-01T08:00:00Z","date_published":"2014-01-01T08:00:00Z","updated_at":"2026-07-24T04:29:15Z","subjects":["Plant Sciences"],"languages":["en"],"rights":["<p>In Copyright - Non-Commercial Use Permitted<br /><a href=\"http://rightsstatements.org/vocab/InC-NC/1.0/\">http://rightsstatements.org/vocab/InC-NC/1.0/</a></p>"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://openprairie.sdstate.edu/etd/1592","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jixiang Wu"]},{"key":"dc:creator","label":"Author","values":["Xu, Yi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-08-16T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Plant Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - University Access Only"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Plant Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["<p>In Copyright - Non-Commercial Use Permitted<br /><a href=\"http://rightsstatements.org/vocab/InC-NC/1.0/\">http://rightsstatements.org/vocab/InC-NC/1.0/</a></p>"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openprairie.sdstate.edu/etd/1592"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["<p>Association mapping has been widely used to detect desirable genetic markers associated with traits of interest for plant improvement. Missing marker data are a common and yet challenging issue in many association mapping studies, especially as the number of markers used for these studies is large. On the other hand, selection of several sets of DNA markers with potential epistasis associated with target traits will greatly help plant improvement via a marker assisted selection approach. In this study, we first proposed a linkage-based imputation method for missing marker data given available linkage information and then integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. The simulation studies showed that both the proposed linkage-based imputation method and MDR-based forward selection method performed well. We also applied these methods to determine SNP (single nucleotide polymorphism) markers with potential epistasis associated with agronomic traits in two crops: barley and wheat.</p>"]},{"key":"dc:title","label":"Title","values":["Genetic Association Mapping : Missing Markers, Epistatic Effects, and Applications"]}]}],"canonical_facts":{"dc:contributor":["Jixiang Wu"],"dc:creator":["Xu, Yi"],"dc:date.available":["2017-08-16T07:00:00Z"],"dc:description.abstract":["<p>Association mapping has been widely used to detect desirable genetic markers associated with traits of interest for plant improvement. Missing marker data are a common and yet challenging issue in many association mapping studies, especially as the number of markers used for these studies is large. On the other hand, selection of several sets of DNA markers with potential epistasis associated with target traits will greatly help plant improvement via a marker assisted selection approach. In this study, we first proposed a linkage-based imputation method for missing marker data given available linkage information and then integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. The simulation studies showed that both the proposed linkage-based imputation method and MDR-based forward selection method performed well. We also applied these methods to determine SNP (single nucleotide polymorphism) markers with potential epistasis associated with agronomic traits in two crops: barley and wheat.</p>"],"dc:identifier":["https://openprairie.sdstate.edu/etd/1592"],"dc:language":["en"],"dc:rights":["<p>In Copyright - Non-Commercial Use Permitted<br /><a href=\"http://rightsstatements.org/vocab/InC-NC/1.0/\">http://rightsstatements.org/vocab/InC-NC/1.0/</a></p>"],"dc:subject":["Plant Sciences"],"dc:title":["Genetic Association Mapping : Missing Markers, Epistatic Effects, and Applications"],"thesis:degree_discipline":["Plant Science"],"thesis:degree_level":["Thesis - University Access Only"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T04:29:15Z"}