Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps

Detalhes bibliográficos
Autor(a) principal: Mollinari, Marcelo
Data de Publicação: 2008
Outros Autores: Margarido, Gabriel Rodrigues Alves, Garcia, Antonio Augusto Franco
Tipo de documento: Artigo
Idioma: por
Título da fonte: Pesquisa Agropecuária Brasileira (Online)
Texto Completo: https://seer.sct.embrapa.br/index.php/pab/article/view/186
Resumo: The objective of this work was to evaluate the efficiency for the construction of genetic linkage maps of the algorithms seriation and rapid chain delineation, as well as the criteria: product of adjacent recombination fractions, sum of adjacent recombination fractions, and sum of adjacent LOD Scores, used with the ripple algorithm. A genetic linkage map was simulated containing 24 markers with random distances between them, with an average of 10 cM. Using the Monte Carlo method, 1,000 backcross populations and 1,000 F2 populations were simulated. The populations comprised 200 individuals each, as well as different combinations of dominant and codominant markers (100% codominant, 100% dominant and mixture containing 50% codominant and 50% dominant). It were also simulated 25, 50 e 75% of missing data. It was observed that both algorithms presented similar performance, and were sensitive to the presence of dominant markers, which makes it difficult to get estimates with good accuracy for both order and distance. Moreover, the algorithm ripple, when applied with the criteria sum of adjacent recombination fractions and product of adjacent recombination fractions, increased the number of correct orders.
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spelling Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage mapsComparação dos algoritmos delineação rápida em cadeia e seriação, para a construção de mapas genéticosripple algorithm; missing data; dominant and codominant markers; Monte Carlo method; QTLalgoritmo ripple; dados perdidos; marcadores dominantes e co-dominantes; método Monte Carlo; QTLThe objective of this work was to evaluate the efficiency for the construction of genetic linkage maps of the algorithms seriation and rapid chain delineation, as well as the criteria: product of adjacent recombination fractions, sum of adjacent recombination fractions, and sum of adjacent LOD Scores, used with the ripple algorithm. A genetic linkage map was simulated containing 24 markers with random distances between them, with an average of 10 cM. Using the Monte Carlo method, 1,000 backcross populations and 1,000 F2 populations were simulated. The populations comprised 200 individuals each, as well as different combinations of dominant and codominant markers (100% codominant, 100% dominant and mixture containing 50% codominant and 50% dominant). It were also simulated 25, 50 e 75% of missing data. It was observed that both algorithms presented similar performance, and were sensitive to the presence of dominant markers, which makes it difficult to get estimates with good accuracy for both order and distance. Moreover, the algorithm ripple, when applied with the criteria sum of adjacent recombination fractions and product of adjacent recombination fractions, increased the number of correct orders.O objetivo deste trabalho foi avaliar a eficiência, na construção de mapas genéticos, dos algoritmos seriação e delineação rápida em cadeia, além dos critérios para avaliação de ordens: produto mínimo das frações de recombinação adjacentes, soma mínima das frações de recombinação adjacentes e soma máxima dos LOD Scores adjacentes, quando usados com o algoritmo de verificação de erros "ripple". Foi simulado um mapa com 24 marcadores, posicionados aleatoriamente a distâncias variadas, com média 10 cM. Por meio do método Monte Carlo, foram obtidas 1.000 populações de retrocruzamento e 1.000 populações F2, com 200 indivíduos cada, e diferentes combinações de marcadores dominantes e co-dominantes (100% co-dominantes, 100% dominantes e mistura com 50% co-dominantes e 50% dominantes). Foi, também, simulada a perda de 25, 50 e 75% dos dados. Observou-se que os dois algoritmos avaliados tiveram desempenho semelhante e foram sensíveis à presença de dados perdidos e à presença de marcadores dominantes; esta última dificultou a obtenção de estimativas com boa acurácia, tanto da ordem quanto da distância. Além disso, observou-se que o algoritmo "ripple" geralmente aumenta o número de ordens corretas e pode ser combinado com os critérios soma mínima das frações de recombinação adjacentes e produto mínimo das frações de recombinação adjacentes.Pesquisa Agropecuaria BrasileiraPesquisa Agropecuária BrasileiraCNPqMollinari, MarceloMargarido, Gabriel Rodrigues AlvesGarcia, Antonio Augusto Franco2008-10-21info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.sct.embrapa.br/index.php/pab/article/view/186Pesquisa Agropecuaria Brasileira; v.43, n.4, abr. 2008; 505-512Pesquisa Agropecuária Brasileira; v.43, n.4, abr. 2008; 505-5121678-39210100-104xreponame:Pesquisa Agropecuária Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAporhttps://seer.sct.embrapa.br/index.php/pab/article/view/186/5414https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/144https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/145https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/146https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/147https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/148https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/149https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/150https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/151https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/164https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/166info:eu-repo/semantics/openAccess2014-05-16T17:40:43Zoai:ojs.seer.sct.embrapa.br:article/186Revistahttp://seer.sct.embrapa.br/index.php/pabPRIhttps://old.scielo.br/oai/scielo-oai.phppab@sct.embrapa.br || sct.pab@embrapa.br1678-39210100-204Xopendoar:2014-05-16T17:40:43Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps
Comparação dos algoritmos delineação rápida em cadeia e seriação, para a construção de mapas genéticos
title Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps
spellingShingle Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps
Mollinari, Marcelo
ripple algorithm; missing data; dominant and codominant markers; Monte Carlo method; QTL
algoritmo ripple; dados perdidos; marcadores dominantes e co-dominantes; método Monte Carlo; QTL
title_short Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps
title_full Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps
title_fullStr Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps
title_full_unstemmed Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps
title_sort Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps
author Mollinari, Marcelo
author_facet Mollinari, Marcelo
Margarido, Gabriel Rodrigues Alves
Garcia, Antonio Augusto Franco
author_role author
author2 Margarido, Gabriel Rodrigues Alves
Garcia, Antonio Augusto Franco
author2_role author
author
dc.contributor.none.fl_str_mv
CNPq
dc.contributor.author.fl_str_mv Mollinari, Marcelo
Margarido, Gabriel Rodrigues Alves
Garcia, Antonio Augusto Franco
dc.subject.por.fl_str_mv ripple algorithm; missing data; dominant and codominant markers; Monte Carlo method; QTL
algoritmo ripple; dados perdidos; marcadores dominantes e co-dominantes; método Monte Carlo; QTL
topic ripple algorithm; missing data; dominant and codominant markers; Monte Carlo method; QTL
algoritmo ripple; dados perdidos; marcadores dominantes e co-dominantes; método Monte Carlo; QTL
description The objective of this work was to evaluate the efficiency for the construction of genetic linkage maps of the algorithms seriation and rapid chain delineation, as well as the criteria: product of adjacent recombination fractions, sum of adjacent recombination fractions, and sum of adjacent LOD Scores, used with the ripple algorithm. A genetic linkage map was simulated containing 24 markers with random distances between them, with an average of 10 cM. Using the Monte Carlo method, 1,000 backcross populations and 1,000 F2 populations were simulated. The populations comprised 200 individuals each, as well as different combinations of dominant and codominant markers (100% codominant, 100% dominant and mixture containing 50% codominant and 50% dominant). It were also simulated 25, 50 e 75% of missing data. It was observed that both algorithms presented similar performance, and were sensitive to the presence of dominant markers, which makes it difficult to get estimates with good accuracy for both order and distance. Moreover, the algorithm ripple, when applied with the criteria sum of adjacent recombination fractions and product of adjacent recombination fractions, increased the number of correct orders.
publishDate 2008
dc.date.none.fl_str_mv 2008-10-21
dc.type.none.fl_str_mv
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://seer.sct.embrapa.br/index.php/pab/article/view/186
url https://seer.sct.embrapa.br/index.php/pab/article/view/186
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://seer.sct.embrapa.br/index.php/pab/article/view/186/5414
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/144
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/145
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/146
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/147
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/148
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/149
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/150
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/151
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/164
https://seer.sct.embrapa.br/index.php/pab/article/downloadSuppFile/186/166
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
publisher.none.fl_str_mv Pesquisa Agropecuaria Brasileira
Pesquisa Agropecuária Brasileira
dc.source.none.fl_str_mv Pesquisa Agropecuaria Brasileira; v.43, n.4, abr. 2008; 505-512
Pesquisa Agropecuária Brasileira; v.43, n.4, abr. 2008; 505-512
1678-3921
0100-104x
reponame:Pesquisa Agropecuária Brasileira (Online)
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reponame_str Pesquisa Agropecuária Brasileira (Online)
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repository.name.fl_str_mv Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
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