Integrating geo-referenced multiscale and multidisciplinary data for the management of biodiversity in livestock genetic resources

P>In livestock genetic resource conservation, decision making about conservation priorities is based on the simultaneous analysis of several different criteria that may contribute to long-term sustainable breeding conditions, such as genetic and demographic characteristics, environmental conditions, and role of the breed in the local or regional economy. Here we address methods to integrate different data sets and highlight problems related to interdisciplinary comparisons. Data integration is based on the use of geographic coordinates and Geographic Information Systems (GIS). In addition to technical problems related to projection systems, GIS have to face the challenging issue of the non homogeneous scale of their data sets. We give examples of the successful use of GIS for data integration and examine the risk of obtaining biased results when integrating datasets that have been captured at different scales.

Breeding Habitat and Landscape Correlates of Frog Diversity and Abundance in a Tropical Agricultural Landscape

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Strategies to improve the efficiency of genomic selection in animal breeding programs

Pós-graduação em Zootecnia - FCAV

Genomic selection and assisted reproduction technologies to foster cattle breeding

Genomics has been propagated as a paradigm shifting innovation in livestock during the last decade. The possibility of predicting breeding values using genomic information has revolutionized the dairy cattle industry and is now being implemented in beef cattle. In this paper we discuss how genomics is changing cattle breeding through genomic selection, and how this change is creating new ways to articulate assisted reproduction technologies with animal breeding. We also debate that the scientific community is still starting the long journey to reveal the functional aspects of the cattle genome, and that knowledge in this field is the frontier to a whole new venue for the development of novel applications in the livestock sector.

The development of genomics applied to dairy breeding

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Predicting breeding values for milk yield of Guzera (Bos indicus) cows using random regression models

Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.

Socio-eco-efficiency of integrated and non-integrated systems of crop, forestry and livestock in the Ipameri city, at brazilian Cerrado

Pós-graduação em Ciências Ambientais - Sorocaba

Study of using marker assisted selection on a beef cattle breeding program by model comparison

A data set of a commercial Nellore beef cattle selection program was used to compare breeding models that assumed or not markers effects to estimate the breeding values, when a reduced number of animals have phenotypic, genotypic and pedigree information available. This herd complete data set was composed of 83,404 animals measured for weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait analyses were performed by MTDFREML software to estimate fixed and random effects solutions using this complete data. The additive effects estimated were assumed as the reference breeding values for those animals. The individual observed phenotype of each trait was adjusted for fixed and random effects solutions, except for direct additive effects. The adjusted phenotype composed of the additive and residual parts of observed phenotype was used as dependent variable for models' comparison. Among all measured animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three models were compared in terms of changes on animals' rank, global fit and predictive ability. Model 1 included only polygenic effects, model 2 included only markers effects and model 3 included both polygenic and markers effects. Bayesian inference via Markov chain Monte Carlo methods performed by TM software was used to analyze the data for model comparison. Two different priors were adopted for markers effects in models 2 and 3, the first prior assumed was a uniform distribution (U) and, as a second prior, was assumed that markers effects were distributed as normal (N). Higher rank correlation coefficients were observed for models 3_U and 3_N, indicating a greater similarity of these models animals' rank and the rank based on the reference breeding values. Model 3_N presented a better global fit, as demonstrated by its low DIC. The best models in terms of predictive ability were models 1 and 3_N. Differences due prior assumed to markers effects in models 2 and 3 could be attributed to the better ability of normal prior in handle with collinear effects. The models 2_U and 2_N presented the worst performance, indicating that this small set of markers should not be used to genetically evaluate animals with no data, since its predictive ability is restricted. In conclusion, model 3_N presented a slight superiority when a reduce number of animals have phenotypic, genotypic and pedigree information. It could be attributed to the variation retained by markers and polygenic effects assumed together and the normal prior assumed to markers effects, that deals better with the collinearity between markers. (C) 2012 Elsevier B.V. All rights reserved.

[Heifer breeding farms as a source for spreading the BVD virus?]

It is well known that, in Switzerland, communal grazing of livestock on alpine pastures plays an important role in the spread of BVD virus. Analogously, we might expect that the communal raising on farms specialising in raising heifers of animals born on different farms would also favour the spread of BVDV. This study investigated whether a persistently infected (PI) breeding heifer kept on this type of farm over a period of 26 months would put the other animals at risk of being infected.The PI-animal was in contact with 75 heifers (here defined as contact animals) on this farm. Thirty-two of the contact animals that were probably pregnant (animals at risk of giving birth to a PI-calf) were moved to 8 different breeding farms (here defined as farms at risk). On these 8 farms, 246 calves were found to be at risk of being infected with BVDV. We examined 78 calves and investigated whether the move of the pregnant animals from their original farm had permitted the virus to spread to these 8 other farms.The contact animals had a seroprevalence of 92% and the animals at risk a seroprevalence of 100%. Only one PI-animal was found on the farms at risk.This BVD infection, however, occurred independently of the PI-breeding animal. Seropositive calves were found only on 2 farms. This study did not provide any proof for a spread of BVDV with the PI-breeding animal as a source; likewise, no persistent infection was proven to exist on the farms at risk. This result is likely to be representative for the endemic situation of BVD in Switzerland. Thus, PI-animals present on heifer raising farms infect calves well before servicing. Hence, no new PI-animals are generated, and the infection becomes self-limiting. When we reconstructed the animal movements between the farms and determined the animals to be examined with the aid of the Swiss national animal traffic database (TVD) we found the data of 37% of the heifers to be incomplete and failed to successfully establish the whereabouts of 3 animals.

A short critical history of the application of genomics to animal breeding

Two scientific schools have been in coexistence from the beginning of genetics, one of them searching for factors of inheritance and the other one applying biometrical models to study the relationships between relatives. With the development of molecular genetics, the possibilities of detecting genes having a noticeable effect in traits augmented. Some genes with large or medium effects were localized in animals, although the most common result was to detect markers linked to these genes, allowing the possibility of assisting selection programs with markers. When a large amount of simple and inexpensive markers were available, the SNPs, new possibilities were opened since they did not need the presence of genes of large or medium effect controlling a trait, because the whole genome was scanned. Using a large amount of SNPs permits having a prediction of the breeding value at birth accurate enough to be used in some cases, like dairy cattle, to halve its generation interval. In other animal breeding programs, the implementation of genomic selection is less clear and the way in which it can be useful should be carefully studied. The need for large populations for associating phenotypic data and markers, plus the need for repeating the process continuously, complicates its application in some cases. The implementation of the information provided by the SNPs in current genetic programs has led to the development of complex statistical tools, joining the efforts of the two schools, factorial and biometrical, that nowadays work closely related.

Principles and methods of animal breeding.

"References to literature": p. [366]-372.

Stock-breeding: a practical treatise on the applications of the laws of development and heredity to the improvement and breeding of domestic animals /

Mode of access: Internet.

Special report on artificial breeding of dairy cattle in California.

Includes bibliography.