scholarly journals Deviance information criterion (DIC) in Bayesian multiple QTL mapping

2009 ◽  
Vol 53 (5) ◽  
pp. 1850-1860 ◽  
Author(s):  
Daniel Shriner ◽  
Nengjun Yi
Keyword(s):  
Qtl Mapping ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Multiple Qtl ◽  
Multiple Qtl Mapping

Multiple QTL mapping in related plant populations via a pedigree-analysis approach

2002 ◽  
Vol 104 (5) ◽  
pp. 751-762 ◽  
Author(s):  
M. Bink ◽  
P. Uimari ◽  
M. Sillanpää ◽  
L. Janss ◽  
R. Jansen
Keyword(s):  
Qtl Mapping ◽  
Pedigree Analysis ◽  
Analysis Approach ◽  
Plant Populations ◽  
Multiple Qtl ◽  
Related Plant ◽  
Multiple Qtl Mapping

scholarly journals Multiple QTL mapping of cardiac collagen deposition in an F2 population of Scn5a mutant mice reveals interaction between Fgf1 and Pdlim3, Gpr158 & Itga6

2013 ◽  
Vol 34 (suppl 1) ◽  
pp. 2602-2602
Author(s):  
E. M. Lodder ◽  
L. Beekman ◽  
D. Arends ◽  
M. E. Adriaens ◽  
B. P. Scicluna ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Mutant Mice ◽  
Collagen Deposition ◽  
F2 Population ◽  
Multiple Qtl ◽  
Multiple Qtl Mapping

scholarly journals Useful properties of Bayesian multiple QTL mapping with MCMC

2006 ◽  
Vol 34 (1) ◽  
pp. 17-29
Author(s):  
Takeshi MIYAKE ◽  
Takahisa YAMADA ◽  
Hirofumi NAKAOKA ◽  
Akira NARITA ◽  
Yoshiyuki SASAKI
Keyword(s):  
Qtl Mapping ◽  
Multiple Qtl ◽  
Multiple Qtl Mapping

scholarly journals R/qtl: high-throughput multiple QTL mapping: Fig. 1.

2010 ◽  
Vol 26 (23) ◽  
pp. 2990-2992 ◽  
Author(s):  
Danny Arends ◽  
Pjotr Prins ◽  
Ritsert C. Jansen ◽  
Karl W. Broman
Keyword(s):  
Qtl Mapping ◽  
High Throughput ◽  
Multiple Qtl ◽  
Multiple Qtl Mapping

scholarly journals Use of Bayesian Markov Chain Monte Carlo Methods to Model Kuwait Medical Genetic Center Data: An Application to Down Syndrome and Mental Retardation

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 248
Author(s):  
Reem Aljarallah ◽  
Samer A Kharroubi
Keyword(s):  
Monte Carlo ◽  
Down Syndrome ◽  
Mental Retardation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Univariate Analysis ◽  
Deviance Information Criterion ◽  
Predictive Ability ◽  
Information Criterion ◽  
Medical Genetic

Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models.

PSXV-9 Transgenerational epigenetic variance for production and reproduction traits in maternal-line pigs

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 258-259
Author(s):  
Jason R Graham ◽  
Jay S Johnson ◽  
Andre C Araujo ◽  
Jeremy T Howard ◽  
Luiz F Brito
Keyword(s):  
Additive Genetic Variance ◽  
Phenotypic Expression ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Pedigree Information ◽  
Relationship Matrix ◽  
Maternal Line ◽  
Genetic Heritability ◽  
Reproduction Traits ◽  
Search Approach

Abstract Modeling epigenetic factors impacting phenotypic expression of economically important traits has become a hot-topic in the field of animal breeding due to the variability in genetic expression caused by environmental stressors (e.g., heat stress). This variability may be due, in part, to in-utero epigenomic remodeling, which has been reported to be passed from parent to offspring. We aimed to estimate transgenerational epigenetic variance for various production and reproduction traits measured in a maternal-line pig population, using a Bayesian approach. The phenotypes for production [n = 10,862; i.e., weaning weight (WW), birth weight (BW) and ultrasound-backfat thickness (BF)] and reproduction [n = 5,235, i.e., number of piglets born alive (NBA) and total number of piglets born (TB)] traits from a purebred Landrace population were provided by Smithfield Premium Genetics (NC, USA). The pedigree information traced back to 10 generations. Single-trait genetic analyses were performed using mixed models that included additive genetic, common environmental, and epigenetic random effects. The Gibbs sampler algorithm based on Markov chain Monte Carlo was used to estimate the variance components. The epigenetic relationship matrix was constructed using a recursive parameter (λ) related to the transmissibility coefficient of epigenetic markers. A grid search approach was used to define the optimal λ value (λ values ranged from 0.1 to 0.5, with an interval of 0.1). The optimal λ value was determined based on the deviance information criterion, and it was used to estimate the additive and epigenetic variances. For instance, based on preliminary results, the optimal λ value estimated for TB was 0.3 with an additive genetic variance of 0.94 (0.19 PSD) and epigenetic variance of 0.67 (0.18 PSD). The additive genetic heritability was 0.076 (0.015 PSD) and the estimated epigenetic heritability was 0.053 (0.015 PSD). This preliminary result suggests that epigenetics contribute to the non-Mendelian variability in pigs.

scholarly journals Utilização da modelagem inteiramente bayesiana na detecção de padrões de variação de risco relativo de mortalidade infantil no Rio Grande do Sul, Brasil

2009 ◽  
Vol 25 (7) ◽  
pp. 1501-1510 ◽  
Author(s):  
Sérgio Kakuta Kato ◽  
Diego de Matos Vieira ◽  
Jandyra Maria Guimarães Fachel
Keyword(s):  
Deviance Information Criterion ◽  
Rio Grande ◽  
Information Criterion ◽  
Rio Grande Do Sul ◽  
Standardised Mortality Ratio ◽  
Mortality Ratio

Neste artigo são analisados os fatores possivelmente associados à mortalidade infantil nos 496 municípios do Rio Grande do Sul, Brasil, com base em dados acumuladas entre os anos de 2001 a 2004, obtidos pela análise de regressão utilizando modelagem inteiramente bayesiana como alternativa para superar a autocorrelação espacial e a instabilidade dos estimadores clássicos, como a taxa bruta e a SMR (Standardised Mortality Ratio). Foram comparadas diferentes especificações de componente espacial e covariáveis, provenientes dos blocos do Índice de Desenvolvimento Sócio-econômico da Fundação de Economia e Estatística (IDESE/FEE-2003). Verificou-se que o modelo que utiliza a estrutura espacial além da covariável educação apresenta melhor desempenho, quando comparado pelo critério DIC (Deviance Information Criterion). Comparando as estimativas das SMR com os riscos relativos obtidos pela modelagem inteiramente bayesiana, foi possível observar um ganho substancial na interpretação e na detecção de padrões de variação do risco de mortalidade infantil nos municípios do Rio Grande do Sul ao utilizar essa modelagem. A região da Serra Gaúcha destacou-se com baixo risco relativo e estimativas muito homogêneas.

Understanding historical summer flounder (Paralichthys dentatus) abundance patterns through the incorporation of oceanography-dependent vital rates in Bayesian hierarchical models

2019 ◽  
Vol 76 (8) ◽  
pp. 1275-1294 ◽  
Author(s):  
Cecilia A. O’Leary ◽  
Timothy J. Miller ◽  
James T. Thorson ◽  
Janet A. Nye
Keyword(s):  
Population Dynamics ◽  
Gulf Stream ◽  
Stock Assessment ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Natural Mortality ◽  
Vital Rates ◽  
Summer Flounder ◽  
Paralichthys Dentatus ◽  
Abundance Patterns

Climate can impact fish population dynamics through changes in productivity and shifts in distribution, and both responses have been observed for many fish species. However, few studies have incorporated climate into population dynamics or stock assessment models. This study aimed to uncover how past variations in population vital rates and fishing pressure account for observed abundance variation in summer flounder (Paralichthys dentatus). The influences of the Gulf Stream Index, an index of climate variability in the Northwest Atlantic, on abundance were explored through natural mortality and stock–recruitment relationships in age-structured hierarchical Bayesian models. Posterior predictive loss and deviance information criterion indicated that out of tested models, the best estimates of summer flounder abundances resulted from the climate-dependent natural mortality model that included log-quadratic responses to the Gulf Stream Index. This climate-linked population model demonstrates the role of climate responses in observed abundance patterns and emphasizes the complexities of environmental effects on populations beyond simple correlations. This approach highlights the importance of modeling the combined effect of fishing and climate simultaneously to understand population dynamics.

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