Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice
ABSTRACT Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bay...
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| Published in | Scientia agricola Vol. 80 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English Portuguese |
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Escola Superior de Agricultura "Luiz de Queiroz"
01.01.2023
Universidade de São Paulo |
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| Abstract | ABSTRACT Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs. |
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| AbstractList | ABSTRACT Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs. |
| Author | Nascimento, Moysés Silva Júnior, Antônio Carlos da Cruz, Cosme Damião Nascimento, Ana Carolina Campana Barreto, Cynthia Aparecida Valiati Suela, Matheus Massariol Azevedo, Camila Ferreira Soraes, Plínio César |
| AuthorAffiliation | Empresa de Pesquisa Agropecuária de Minas Gerais Universidade Federal de Viçosa |
| AuthorAffiliation_xml | – name: Universidade Federal de Viçosa – name: Empresa de Pesquisa Agropecuária de Minas Gerais |
| Author_xml | – sequence: 1 givenname: Camila Ferreira orcidid: 0000-0003-0438-5123 surname: Azevedo fullname: Azevedo, Camila Ferreira organization: Universidade Federal de Viçosa, Brasil – sequence: 2 givenname: Cynthia Aparecida Valiati orcidid: 0000-0003-0474-4587 surname: Barreto fullname: Barreto, Cynthia Aparecida Valiati organization: Universidade Federal de Viçosa, Brasil – sequence: 3 givenname: Matheus Massariol orcidid: 0000-0001-5406-7292 surname: Suela fullname: Suela, Matheus Massariol organization: Universidade Federal de Viçosa, Brasil – sequence: 4 givenname: Moysés orcidid: 0000-0001-5886-9540 surname: Nascimento fullname: Nascimento, Moysés organization: Universidade Federal de Viçosa, Brasil – sequence: 5 givenname: Antônio Carlos da orcidid: 0000-0002-4200-6182 surname: Silva Júnior fullname: Silva Júnior, Antônio Carlos da organization: Universidade Federal de Viçosa, Brasil – sequence: 6 givenname: Ana Carolina Campana orcidid: 0000-0002-6985-1490 surname: Nascimento fullname: Nascimento, Ana Carolina Campana organization: Universidade Federal de Viçosa, Brasil – sequence: 7 givenname: Cosme Damião orcidid: 0000-0003-3513-3391 surname: Cruz fullname: Cruz, Cosme Damião organization: Universidade Federal de Viçosa, Brasil – sequence: 8 givenname: Plínio César orcidid: 0000-0001-9339-0463 surname: Soraes fullname: Soraes, Plínio César organization: Empresa de Pesquisa Agropecuária de Minas Gerais, Brasil |
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| Cites_doi | 10.18637/jss.v033.i02 10.1590/0103-8478cr20181008 10.1016/j.fcr.2020.107929 10.1155/2018/8936767 10.1038/s41588-019-0496-6 10.3390/agronomy10010075 10.1007/s10681-022-02995-0 10.1038/s43586-020-00001-2 10.1534/genetics.106.061549 10.1007/s00122-013-2089-6 10.1371/journal.pone.0247775 10.1371/journal.pone.0208871 10.1371/journal.pone.0199492 10.1371/journal.pone.0066428 10.1186/1471-2288-5-32 10.1093/aje/153.12.1222 10.1590/1983-40632018v4851950 10.12702/1984-7033.v05n02a03 10.1186/s12919-018-0131-z 10.1093/biosci/bix010 10.1111/rssb.12062 10.1093/g3journal/jkab178 10.3390/agronomy10081098 10.1371/journal.pone.0259607 |
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| Keywords | MCMC prior distribution genetic correlation genetic improvement heritability |
| Language | English Portuguese |
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| Title | Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice |
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