Estimated genetic associations among reproductive traits in Nellore cattle using Bayesian analysis

2020 ◽  
Vol 214 ◽  
pp. 106305 ◽  
Author(s):  
Edson V. Costa ◽  
Henrique T. Ventura ◽  
Renata Veroneze ◽  
Fabyano F. Silva ◽  
Mariana A. Pereira ◽  
...  
2012 ◽  
Vol 11 (3) ◽  
pp. 2979-2986 ◽  
Author(s):  
I.C. Regatieri ◽  
A.A. Boligon ◽  
F. Baldi ◽  
L.G. Albuquerque

animal ◽  
2012 ◽  
Vol 6 (4) ◽  
pp. 565-570 ◽  
Author(s):  
M.L. Santana ◽  
J.P. Eler ◽  
J.B.S. Ferraz ◽  
E.C. Mattos

Author(s):  
Leonardo Martin Nieto ◽  
Luiz Otávio Campos da Silva ◽  
Antônio do Nascimento Ferreira Rosa

Abstract: The objective of this work was to evaluate the potential of different threshold models to determine the genetic variability in Nellore cattle, with basis on the heritability estimates for the traits stayability (STA) and first calving probability at 36 months of age (CP36). Data came from the Nellore herds participating in the animal breeding program of the Embrapa-Geneplus partnership. Binomial and multi-threshold models were defined for the STA and CP36 traits. Heritability estimates were obtained following Bayesian procedures in the Multiple-trait Gibbs Sampler for Animal Models (MTGSAM) software, using a sire-maternal grandsire model. The heritability estimates, provided by the binary and alternative models, were, respectively, 0.08 and 0.12 for STA and 0.17 and 0.12 for CP36. The multi-threshold model can efficiently detect the genetic variability for stayability, but not for probability of calving for 36-month-old cows.


2018 ◽  
Vol 217 ◽  
pp. 37-43 ◽  
Author(s):  
Giovana Vargas ◽  
Flavio Schramm Schenkel ◽  
Luiz Fernando Brito ◽  
Haroldo Henrique de Rezende Neves ◽  
Danísio Prado Munari ◽  
...  

2016 ◽  
Vol 48 (7) ◽  
pp. 1401-1407 ◽  
Author(s):  
Diego Pagung Ambrosini ◽  
Carlos Henrique Mendes Malhado ◽  
Raimundo Martins Filho ◽  
Fernando Flores Cardoso ◽  
Paulo Luiz Souza Carneiro

animal ◽  
2012 ◽  
Vol 6 (1) ◽  
pp. 36-40 ◽  
Author(s):  
D. Barrozo ◽  
M.E. Buzanskas ◽  
J.A. Oliveira ◽  
D.P. Munari ◽  
H.H.R. Neves ◽  
...  

2007 ◽  
Vol 38 (1) ◽  
pp. 67-75 ◽  
Author(s):  
S. P. Turner ◽  
R. Roehe ◽  
W. Mekkawy ◽  
M. J. Farnworth ◽  
P. W. Knap ◽  
...  

2017 ◽  
Author(s):  
Adrian Cortes ◽  
Calliope A. Dendrou ◽  
Allan Motyer ◽  
Luke Jostins ◽  
Damjan Vukcevic ◽  
...  

Genetic discovery from the multitude of phenotypes extractable from routine healthcare data has the ability to radically transform our understanding of the human phenome, thereby accelerating progress towards precision medicine. However, a critical question when analysing high-dimensional and heterogeneous data is how to interrogate increasingly specific subphenotypes whilst retaining statistical power to detect genetic associations. Here we develop and employ a novel Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to jointly analyse genetic variants against UK Biobank healthcare phenotypes. Our method displays a more than 20% increase in power to detect genetic effects over other approaches, such that we uncover the broader burden of genetic variation: we identify associations with over 2,000 diagnostic terms. We find novel associations with common immune-mediated diseases (IMD), we reveal the extent of genetic sharing between specific IMDs, and we expose differences in disease perception or diagnosis with potential clinical implications.


Author(s):  
A.A. Boligon ◽  
D.R. Ayres ◽  
R.J. Pereira ◽  
N.P. Morotti ◽  
L.G. Albuquerque

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