Bayesian Analysis of the Genetic Control of Survival in F3 Families of Common Bean

Mora, Freddy; Gonçalves‐Vidigal, Maria Celeste; Santos, Alexandra Inês dos

doi:10.4067/s0718-58392008000400003

Cited by 5 publications

(6 citation statements)

References 23 publications

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“…Single-trait analyses were conducted for spawning success, multiple spawning, spawning frequency, and volume of eggs through Bayesian inference and via Gibbs sampling, which uses the Monte Carlo Markov chain method (Mora et al 2008). Statistical analyses were performed using the MTGSAM Threshold (Multiple Trait Gibbs Sampler for Animal Models) software (Van Tassel et al 1998).…”

Section: Methodsmentioning

confidence: 99%

Genetic parameters in female reproductive traits of Nile tilapia (Oreochromis niloticus)

Yoshida

Oliveira²,

Kunita³

et al. 2017

An. Acad. Bras. Ciênc.

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Genetic parameters for reproductive traits in female Nile tilapia were estimated in this study using Bayesian inference method. The data set presented information from 451 Nile tilapia females that were evaluated at two different places in Maringá -Paraná -Brazil (hapas of 1 and 10 m³) and at one location in Alfenas -Minas Gerais -Brazil. A spawning examination was conducted once a week from October 2012 to March 2013 for a total of 23 weeks of evaluation. Single-trait analyses for spawning success, multiple spawning, spawning frequency, and volume of eggs/female were performed by using the software MTGSAM Threshold. The heritability estimates were 0.14, 0.16, 0.53, and 0.08 for spawning success, multiple spawning, spawning frequency and volume of eggs/female, respectively, indicating it is possible to achieve a substantial genetic gain using these reproductive traits as selection criteria.

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Section: Methodsmentioning

confidence: 99%

Genetic parameters in female reproductive traits of Nile tilapia (Oreochromis niloticus)

Yoshida

Oliveira²,

Kunita³

et al. 2017

An. Acad. Bras. Ciênc.

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“…The analyses were done using the Bayesian approach, via Gibbs sampling, which is a variant of the Monte-Carlo Markov chain methods (Mora et al, 2008). The threshold version of the MTGSAM program (Van Tassell et al, 1998) was used to formulate the posterior distribution of each genetic parameter.…”

Section: Methodsmentioning

confidence: 99%

Bayesian genetic parameters for body weight and survival of Nile tilapia farmed in Brazil

Santos

Ribeiro

Vargas

et al. 2011

Pesq. agropec. bras.

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-The objective of this study was to estimate genetic parameters for survival and weight of Nile tilapia (Oreochromis niloticus), farmed in cages and ponds in Brazil, and to predict genetic gain under different scenarios. Survival was recorded as a binary response (dead or alive), during harvest time in the 2008 grow-out period. Genetic parameters were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. The breeding population consisted of 2,912 individual fish, which were analyzed together with the pedigree of 5,394 fish. The heritabilities estimates, with 95% posterior credible intervals, for tagging weight, harvest weight and survival were 0.17 (0.09-0.27), 0.21 (0.12-0.32) and 0.32 (0.22-0.44), respectively. Credible intervals show a 95% probability that the true genetic correlations were in a favourable direction. The selection for weight has a positive impact on survival. Estimated genetic gain was high when selecting for harvest weight (5.07%), and indirect gain for tagging weight (2.17%) and survival (2.03%) were also considerable.Index terms: Oreochromis niloticus, correlation, fish genetic improvement, Gibbs sampling, GIFT strain, heritability. Parâmetros genéticos bayesianos para peso corporal e sobrevivência de tilápias-do-nilo cultivadas no BrasilResumo -O objetivo deste trabalho foi estimar parâmetros genéticos para a sobrevivência e peso de tilápias-do-nilo (Oreochromis niloticus), cultivadas em tanques-rede e viveiro de terra no Brasil, e predizer o ganho genético sob diferentes cenários. Mediu-se a sobrevivência como uma característica binária (peixe vivo ou morto), no período de crescimento de 2008 até a despesca. Os paramêtros genéticos foram estimados pelo modelo animal limiar-linear em análise bayesiana, pela amostragem de Gibbs. A população avaliada foi de 2.912 peixes individuais, que foram analisados juntamente com o pedigree de 5.394 peixes. As herdabilidades, com intervalos de credibilidade de 95%, obtidas para peso à identificação, peso à despesca e sobrevivência foram 0,17 (0,09-0,27), 0,21 (0,12-0,32) e 0,32 (0,22-0,44), respectivamente. Intervalos de credibilidade mostram 95% de probabilidade de que as verdadeiras correlações genéticas estejam em uma direção favorável. A seleção para peso tem impacto positivo na sobrevivência. O ganho genético estimado foi alto para a seleção quanto ao peso à despesca (5,07%), e o ganho indireto para o peso à identificação (2,17%) e à sobrevivência (2,03%) também foram consideráveis.Termos para indexação: Oreochromis niloticus, correlação, melhoramento genético de peixes, amostragem de Gibbs, linhagem GIFT, herdabilidade.

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“…For example, binary data are often found when the aim is to improve traits such as disease status (Setiawan et al, 2000;Park et al, 2001), mortality or survival (Mora et al, 2008d), flowering (Missiaggia et al, 2005;Mora et al, 2007;Mora et al, 2009), among other traits. Yang et al (2009) stated that deviations from this assumption may affect the accuracy of QTL detection and lead to detection of spurious QTLs.…”

Section: Resultsmentioning

confidence: 99%

“…The current study has been motivated by the existence of traits of interest with other than Gaussian distributions (Mora et al, 2007;Mora et al, 2008b;Rodovalho et al, 2008;Mora et al, 2008d). Therefore, this study aimed to map a quantitative trait locus by using a generalized linear regression modeling approach where the agronomical trait is non-normally distributed.…”

Section: Introductionmentioning

confidence: 99%

Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits

Mora¹,

Scapim

Baharum

et al. 2010

Cienc. Inv. Agr.

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In genetic studies, most Quantitative Trait Loci (QTL) mapping methods presuppose that the continuous trait of interest follows a normal (Gaussian) distribution. However, many economically important traits of agricultural crops have a non-normal distribution. Composite interval mapping (CIM) has been successfully applied to the detection of QTL in animal and plant breeding. In this study we report a generalized CIM (GCIM) method that permits QTL analysis of non-normally distributed variables. GCIM was based on the classic Generalized Linear Model method. We applied the GCIM method to a F 2 population with co-dominant molecular markers and the existence of a QTL controlling a trait with Gamma distribution. Computer simulations indicated that the GCIM method has superior performance in its ability to map QTL, compared with CIM. QTL position differed by 5 cM and was located at different marker intervals. The Likelihood Ratio Test values ranged from 52 (GCIM) to 76 (CIM). Thus, wrongly assuming CIM may overestimate the effect of the QTL by about 47%. The usage of GCIM methodology can offer improved efficiency in the analysis of QTLs controlling continuous traits of non-Gaussian distribution.

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Bayesian Analysis of the Genetic Control of Survival in F3 Families of Common Bean

Cited by 5 publications

References 23 publications

Genetic parameters in female reproductive traits of Nile tilapia (Oreochromis niloticus)

Genetic parameters in female reproductive traits of Nile tilapia (Oreochromis niloticus)

Bayesian genetic parameters for body weight and survival of Nile tilapia farmed in Brazil

Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits

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