{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/70021"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/70021","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Selection and Assortative Mating","abstract":"Theoretical aspects of selection and assortative mating were considered separately and jointly. Selection based on the conditional mean of the merit variable given the data maximizes the expected value of the merit variable when the proportion selected is constant. This is optimal even if candidates to be evaluated and the available information are a function of previous selection decisions. Two-stage selection maximizing the expected merit of candidates selected in the second stage, was developed. Recursive formulae were derived for phenotypic and genetic variances and covariances after t generations of assortative mating based on linear combinations of phenotypic values of two traits (mating rules). Selection efficiency after t generations of assortative mating without selection was expressed as a function of the mating rule, and the rule maximizing selection efficiency was obtained numerically. For two examples considered, the optimum rule increased selection efficiency by 29% and 17% after 31 and 36 generations. Also, the sign of the genetic correlation could be changed by assortative mating on linear combinations of traits. Models with two alleles per locus (Model A) or an infinite number of alleles per locus (Model B) were used to examine jointly assortative mating and selection, assuming a population of infinite size for each model. The effects of assortative mating on genetic change was greater when heritability in the base population and the proportion selected were high and the initial gene frequency was low. The effect of number of loci was obscure with Model A. With Model B, the advantage of assortative mating over random mating increased with number of loci. Assortative mating and selection in a finite population were studied for a model with two alleles per locus using computer simulation. Results were similar to those obtained from Model A.","abstract_html":"Theoretical aspects of selection and assortative mating were considered separately and jointly. Selection based on the conditional mean of the merit variable given the data maximizes the expected value of the merit variable when the proportion selected is constant. This is optimal even if candidates to be evaluated and the available information are a function of previous selection decisions. Two-stage selection maximizing the expected merit of candidates selected in the second stage, was developed. Recursive formulae were derived for phenotypic and genetic variances and covariances after t generations of assortative mating based on linear combinations of phenotypic values of two traits (mating rules). Selection efficiency after t generations of assortative mating without selection was expressed as a function of the mating rule, and the rule maximizing selection efficiency was obtained numerically. For two examples considered, the optimum rule increased selection efficiency by 29% and 17% after 31 and 36 generations. Also, the sign of the genetic correlation could be changed by assortative mating on linear combinations of traits. Models with two alleles per locus (Model A) or an infinite number of alleles per locus (Model B) were used to examine jointly assortative mating and selection, assuming a population of infinite size for each model. The effects of assortative mating on genetic change was greater when heritability in the base population and the proportion selected were high and the initial gene frequency was low. The effect of number of loci was obscure with Model A. With Model B, the advantage of assortative mating over random mating increased with number of loci. Assortative mating and selection in a finite population were studied for a model with two alleles per locus using computer simulation. Results were similar to those obtained from Model A.","abstract_has_math":false,"creators":["Fernando, Rohan Luigi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Animal Sciences","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-15T21:00:57Z","date_published":"2014-12-15T21:00:57Z","updated_at":"2026-07-22T22:26:02Z","subjects":["Biology, Genetics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8502139"],"render_values":[{"text":"(UMI)AAI8502139","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/70021","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Fernando, Rohan Luigi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-15T21:00:57Z","10000-01-01","1984"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Animal 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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biology, Genetics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/70021","(UMI)AAI8502139"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Theoretical aspects of selection and assortative mating were considered separately and jointly. Selection based on the conditional mean of the merit variable given the data maximizes the expected value of the merit variable when the proportion selected is constant. This is optimal even if candidates to be evaluated and the available information are a function of previous selection decisions. Two-stage selection maximizing the expected merit of candidates selected in the second stage, was developed. Recursive formulae were derived for phenotypic and genetic variances and covariances after t generations of assortative mating based on linear combinations of phenotypic values of two traits (mating rules). Selection efficiency after t generations of assortative mating without selection was expressed as a function of the mating rule, and the rule maximizing selection efficiency was obtained numerically. For two examples considered, the optimum rule increased selection efficiency by 29% and 17% after 31 and 36 generations. Also, the sign of the genetic correlation could be changed by assortative mating on linear combinations of traits. Models with two alleles per locus (Model A) or an infinite number of alleles per locus (Model B) were used to examine jointly assortative mating and selection, assuming a population of infinite size for each model. The effects of assortative mating on genetic change was greater when heritability in the base population and the proportion selected were high and the initial gene frequency was low. The effect of number of loci was obscure with Model A. With Model B, the advantage of assortative mating over random mating increased with number of loci. Assortative mating and selection in a finite population were studied for a model with two alleles per locus using computer simulation. Results were similar to those obtained from Model A.","Made available in DSpace on 2014-12-15T21:00:57Z (GMT). No. of bitstreams: 1 8502139.pdf: 4093038 bytes, checksum: df25568e948a823c6e8568972c5dc262 (MD5) Previous issue date: 1984","Embargo set by: Seth Robbins for item 70187 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","151 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1984."]},{"key":"dc:title","label":"Title","values":["Selection and Assortative Mating"]}]}],"canonical_facts":{"dc:creator":["Fernando, Rohan Luigi"],"dc:date":["2014-12-15T21:00:57Z","10000-01-01","1984"],"dc:description":["Theoretical aspects of selection and assortative mating were considered separately and jointly. Selection based on the conditional mean of the merit variable given the data maximizes the expected value of the merit variable when the proportion selected is constant. This is optimal even if candidates to be evaluated and the available information are a function of previous selection decisions. Two-stage selection maximizing the expected merit of candidates selected in the second stage, was developed. Recursive formulae were derived for phenotypic and genetic variances and covariances after t generations of assortative mating based on linear combinations of phenotypic values of two traits (mating rules). Selection efficiency after t generations of assortative mating without selection was expressed as a function of the mating rule, and the rule maximizing selection efficiency was obtained numerically. For two examples considered, the optimum rule increased selection efficiency by 29% and 17% after 31 and 36 generations. Also, the sign of the genetic correlation could be changed by assortative mating on linear combinations of traits. Models with two alleles per locus (Model A) or an infinite number of alleles per locus (Model B) were used to examine jointly assortative mating and selection, assuming a population of infinite size for each model. The effects of assortative mating on genetic change was greater when heritability in the base population and the proportion selected were high and the initial gene frequency was low. The effect of number of loci was obscure with Model A. With Model B, the advantage of assortative mating over random mating increased with number of loci. Assortative mating and selection in a finite population were studied for a model with two alleles per locus using computer simulation. Results were similar to those obtained from Model A.","Made available in DSpace on 2014-12-15T21:00:57Z (GMT). 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