Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , |
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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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) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Pesquisa Agropecuária Brasileira (Online) |
collection |
Pesquisa Agropecuária Brasileira (Online) |
repository.name.fl_str_mv |
Pesquisa Agropecuária Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
pab@sct.embrapa.br || sct.pab@embrapa.br |
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1793416658321145856 |