Source: UNIV OF WISCONSIN submitted to
INFERRING CAUSAL PHENOTYPE NETWORKS IN LIVESTOCK USING GENOMIC INFORMATION
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
TERMINATED
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
0224699
Grant No.
2011-67015-30219
Project No.
WIS01563
Proposal No.
2010-04534
Multistate No.
(N/A)
Program Code
A1201
Project Start Date
Mar 15, 2011
Project End Date
Mar 14, 2016
Grant Year
2011
Project Director
ROSA, G. J.
Recipient Organization
UNIV OF WISCONSIN
21 N PARK ST STE 6401
MADISON,WI 53715-1218
Performing Department
Animal Sciences
Non Technical Summary
The success of livestock enterprises depends on a number of traits related to production and quality of animal products, and understanding the relationships among them is crucial for efficient decision making regarding farm practices and breeding strategies. For example, high yield in dairy cows increases susceptibility to certain diseases and, conversely, the presence of disease may adversely affect yield. However, the relationships among phenotypic traits have been traditionally studied through the estimation of their correlations. Although knowledge regarding correlations can be used satisfactorily to infer how probable events are, they are not suitable to predict how probabilities would change as a result of external interventions. For instance, a correlation between traits A and B can be due to a direct effect of A on B (or B on A) or to extraneous variables that jointly affect A and B. Knowledge about the causal structure underlying phenotypic relationships is necessary to predict the effect of management practices applied to trait A or B. For example, if trait A affects B, and B has no effect on A, an intervention on A will cause changes on B, but the reverse would not hold true. The goal of this project is to develop and apply statistical methods and computer algorithms for inferring phenotype networks and causal relationships between traits of economic importance in livestock. The specific objectives of this project include the development and application of statistical and computational tools for phenotype network reconstruction and causal inference using the genomic information increasingly available for livestock species; and the assembling of the developed computational algorithms into specific computer packages to be made freely available to the public. Expected results from this project include the generation of novel methodologies for the analysis of livestock data on multiple traits, which will bring further insight on the phenotype network and causal relationships among them. In addition, the effectiveness of using genomic information to aid inferring phenotypic traits relationship will be evaluated. It is expected that this project will provide geneticists and animal breeders with a paradigmatic shift on the way genetic data is analyzed, moving from traditional statistical modeling to causal analysis of multivariate data. Lastly, a package of computer programs will be made freely available to interested users, making it possible for anyone to implement causal inference on multi-trait data. As such, the proposed research will not only contribute to the advance of fundamental animal breeding and genetics sciences with the development of data mining tools for studying relationships among phenotypes, but also its outcomes may influence the way multi-trait analyses of agricultural data are performed, and how the results are translated into farm management practices and decision making, contributing to the improvement and sustainability of U.S. agriculture and food systems.
Animal Health Component
(N/A)
Research Effort Categories
Basic
90%
Applied
10%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
3053899106050%
3043520108020%
3033410108020%
3073899108010%
Knowledge Area
305 - Animal Physiological Processes; 307 - Animal Management Systems; 303 - Genetic Improvement of Animals; 304 - Animal Genome;

Subject Of Investigation
3520 - Meat, swine; 3899 - Other animals, general; 3410 - Dairy cattle, live animal;

Field Of Science
1080 - Genetics; 1060 - Biology (whole systems);
Goals / Objectives
In biological systems, phenotypic traits may exert mutual effects which can be studied using statistical models that account for recursiveness and feedback. For example, high yield in dairy cows increases susceptibility to certain diseases and, conversely, the presence of disease may adversely affect yield. Likewise, the transcriptome may be a function of reproductive status in mammals and the latter may depend on other physiological variables. Knowledge of phenotype networks describing such interrelationships can be used to predict behavior of complex systems, for example, the biological pathways underlying complex traits such as diseases, growth and reproduction. Similarly, in a more pragmatic context, the success of agricultural systems depends on a number of traits related to production and quality of animal and plant products, and understanding the relationships among them is crucial for efficient decision making regarding farm practices and breeding strategies. However, traditional statistical models used in agriculture focus on the estimation of correlations between traits, and therefore they are not capable of predicting the effect of external interventions. Surprisingly, although hardly known to empirical researchers, much of the conceptual and algorithmic tools related to causal models are well established in other sciences. The goal of this project is to develop and apply statistical methods and computer algorithms for inferring phenotype networks and causal relationships between traits of economic importance in livestock, and to study how knowledge about causality can be used to advance management practices and decision making in livestock production and genetic improvement. The specific objectives of this project are: 1) Develop and apply statistical and computational tools for phenotype network reconstruction and causal inference in genetical genomics studies with livestock; 2) Extend available statistical and computational methods for searching for recursive causal structures in multivariate quantitative genetics mixed models, and apply them to livestock data; 3) Explore the use of genomic information to more effectively adjust phenotypic data for genetic effects for increased power of detection of causal effects; and 4) Assemble the developed computational algorithms into specific R packages to be made available through the R Project website together with a user's manual.
Project Methods
Structural equation models (SEM) will make up the core of the statistical methods to be considered in this project. Bayesian methods employing Markov Chain Monte Carlo (MCMC) tools will be used for fitting such models. In addition, specific algorithms will be employed for data driven selection of causal structures. Fitting SEM within a multi-trait mixed model methodology with a known (or pre-selected) causal structure can be easily implemented using Bayesian MCMC methods. The novelty of the methods to be used in this project refers to the search and selection of the most plausible causal structure (or phenotype network) among all possible structures involving the phenotypic traits of interest. Such causal structure search will be implemented using data-driven algorithms, such as the Inductive Causation (IC) algorithm. The IC algorithm is based on queries about conditional independencies between variables to try to recover a causal structure or a class of equivalent causal structures (causal structures that result in joint probability distributions with the same conditional independence relationships) from the joint distribution of the data. As such, the input of the algorithm is a correlation matrix between observable variables, from which marginal and conditional dependencies can be evaluated, whereas the output is a partially oriented graph representing a class of equivalent causal structures, which generally results on an important constraint on the initial causal hypothesis space that could be used to fit the SEM. Partially-oriented graphs are graphs with directed and undirected edges. The latter represent symmetric direct relationships between pairs of variables, since they do not specify direction of causal relationship. An additional aspect of the methods to be implemented in this project relates to the fact that patterns of co-variability between phenotypes in the animal breeding context may be due either to causal links between traits or to genetic reasons. In other words, correlated random genetic effects can act as confounders if one tries to select a causal structure based on the joint distribution of the phenotypes even if residuals are assumed as independent. Nonetheless, additive relationships between individuals give a means of "controlling" for this confounder. This can be done, for example, if there is pedigree or molecular marker information on the individuals; both situations will be considered in this project (please refer to Specific Objectives 1-3). In summary, the overall statistical approach to be used throughout the project will consist of three stages: 1. Traditional Bayesian multi-trait model will be fitted, using either QTL, pedigree, or molecular marker information and posterior samples of the residual covariance matrix will be obtained. 2. The IC algorithm will be applied to the posterior samples of the residual covariance matrix to make the statistical decisions required. 3. Lastly, a SEM using the selected causal structure (or one member within the class of observationally equivalent structures retrieved by the IC algorithm) will be fitted and causal relationships (i.e., recursive effects) will be estimated.

Progress 03/15/11 to 03/14/16

Outputs
Target Audience:The project was essentially of basic knowledge development and as such its target audience refers to individuals from the scientific community and industry who develop research. Nevertheless, the applications of the methods developed in this project allow applied researchers from various disciplines of animal sciences to better understand causal relationships between phenotypic traits such that better informed decisions regarding management interventions (e.g. improvement in diet, housing, reproductive program, etc.) is possible, targeting the optimization of resources utilization and sustainability of livestock enterprises. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project had a PhD student (Mr. Francisco Penagaricano) and had also a post-doctoral fellow (Dr. Bruno Valente), who were directly involved with and supported by this project. In addition, the project has attracted international visiting scholars and visiting scientists to the University of Wisconsin-Madison: Mrs. Aniek Bowman, from the Wageningen University, Netherlands; Mr. Raphael Rocha, from the Federal University of Minas Gerais, Brazil; and Mr. Keiichi Inoue, from the Kyoto University in Japan; and Dr. Angelina Fraga, Federal University of Alagoas, Brazil; and Dr. Fernando Brito, from the Brazilian Agricultural Research Corporation (EMBRAPA), Brazil. These students/scientists however were supported by their own institutions. How have the results been disseminated to communities of interest?The project results have been disseminated to communities of interest via peer-reviewed scientific publications, review papers and book chapters, as well as invited talks at scientific meetings and seminars, both in the US and abroad. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? During the fifth year of this project, we finalized a number of parallel projects related to the analysis of datasets on meat quality in beef cattle and on pigs as well as reproductive traits in quails. The beef cattle data comprised measurements/assessment of beef marbling score, beef color score, firmness of beef, texture of beef, beef fat color score, and the ratio of saturated fatty acids to mono unsaturated fatty acids from 11,855 fattening Japanese Black cattle. Our overall goal was to investigate how these traits are functionally related to each other such that optimized breeding goals and management practices for improvement of meat quality traits can be developed. Similar goal was sought with the pig study, in which data from a three-generation Duroc x Pietrain resource population was utilized including growth, carcass and meat quality records. In both the beef cattle and the pig studies, we investigated the functional relationships among economically important traits using the statistical and computational approaches developed in our project to model the network involving those traits. Lastly, with the quail data we used network approaches to develop a statistical model to predict the total egg production of individual birds based on earlier expressed phenotypes such as growth and reproductive traits.

Publications

  • Type: Book Chapters Status: Awaiting Publication Year Published: 2016 Citation: Rosa, G. J. M., Felipe, V. P. S. and Pe�agaricano, F. Applications of Graphical Models in Quantitative Genetics and Genomics. In: Systems Biology in Animal Production and Health, Volume 1. Kadarmideen, H. (Ed.) Springer (in press).
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Pe�agaricano, F., Valente, B. D., Steibel, J. P., Bates, R. O., Ernst, C. W., Khatib, H. and Rosa, G. J. M. Exploring causal networks underlying fat deposition and muscularity in pigs through the integration of phenotypic, genotypic and transcriptomic data. BMC Systems Biology 9: 58, 2015.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Valente, B. D., Morota, G., Pe�agaricano, F., Gianola, D., Weigel, K. A. and Rosa, G. J. M. The causal meaning of genomic predictors and how it affects the construction and comparison of genome-enabled selection models. Genetics 200: 483-494, 2015.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Felipe, V. P. S., Silva, M. A., Valente, B. D. and Rosa, G. J. M. Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes. Poultry Science 94: 772-780, 2015.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Pe�agaricano, F., Valente, B. D., Steibel, J. P., Bates, R. O., Ernst, C. W., Khatib, H. and Rosa, G. J. M. Searching for causal networks involving latent variables in complex traits: Application to growth, carcass, and meat quality traits in pigs. Journal of Animal Science 93: 912-919, 2015.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2015 Citation: Rosa, G. J. M., Felipe, V. and Pe�agaricano, F. Applications of graphical models in quantitative genetics and genomics (full paper). In: 64th National Poultry Breeders Roundtable, Saint Louis, Missouri, May 7-8, 2015.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2015 Citation: Rosa, G.J.M. Invited talk: Applications of Graphical Models in Quantitative Genetics and Genomics, at the 2015 National Breeders Roundtable, Saint Louis - MO, May 7-8, 2015.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2015 Citation: Rosa, G. J.M.Invited talk: Is Complex Modeling Important in the Age of Genomic Selection?, at the ADSA-ASAS Joint Annual Meeting, Orlando - FL, July 12-16, 2015.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2016 Citation: Rosa,G.J.M. Invited talk: Modeling Networks for Prediction and Causal Inference in Quantitative Genetics and Genomics, at the ADSA-ASAS Midwest Meeting, Des Moines - Iowa, March 14-16, 2016.


Progress 03/15/14 to 03/14/15

Outputs
Target Audience: This project is essentially of basic knowledge development and as such its target audience refers to individuals from the scientific community and industry that develop research. However, the applications of the methods developed in this project will allow applied researchers from various disciplines of animal sciences to better understand causal relationships between phenotypic traits such that better informed decisions regarding management interventions (e.g. improvement in diet, housing, reproductive program, etc.) will be possible, targeting the optimization of resources utilization and sustainability of livestock enterprises. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? The project has a PhD student (Mr. Francisco Penagaricano) and also had a post-doctoral fellow (Dr. Bruno Valente), who are/were directly involved with and supported by this project. In addition, the project has attracted international visiting scholars and visiting scientists to the University of Wisconsin-Madison: Mrs. Aniek Bowman, from the Wageningen University, Netherlands; Mr. Raphael Rocha, from the Federal University of Minas Gerais, Brazil; and Mr. Keiichi Inoue, from the Kyoto University in Japan; and Dr. Angelina Fraga, Federal University of Alagoas, Brazil; and Dr. Fernando Brito, from the Brazilian Agricultural Research Corporation (EMBRAPA), Brazil. These students/scientists however were supported by their own institutions. How have the results been disseminated to communities of interest? The project results have been disseminated to communities of interest via peer-reviewed scientific publications, review papers and book chapters, as well as invited talks at scientific meetings and seminars, both in the US and abroad. What do you plan to do during the next reporting period to accomplish the goals? For the next period, we will finish ongoing statistical analysis of multiple trait datasets in various species and submit results for publication in scientific journals. The datasets refer to three independent studies, two related to meat quality (one in beef cattle and another in pigs) and a third related to reproductive traits in quails. Ongoing analyses refer to the application of the network models developed in this project for better understanding functional relationships among economically important traits in livestock production, so that optimized breeding goals and management practices can be developed.

Impacts
What was accomplished under these goals? During the fourth year of this project, we started a number of parallel projects related to the analysis of datasets on meat quality in beef cattle and on pigs as well as reproductive traits in quails. The beef cattle data comprise of measurements/assessment of beef marbling score, beef color score, firmness of beef, texture of beef, beef fat color score, and the ratio of saturated fatty acids to mono unsaturated fatty acids from 11,855 fattening Japanese Black cattle. Our overall goal is to investigate how these traits are functionally related to each other, such that optimized breeding goals and management practices for improvement of meat quality traits can be developed. A similar goal is sought with the pig study, in which data from a three-generation Duroc x Pietrain resource population is available with growth, carcass and collected meat quality phenotypes. In both the beef cattle and the pig studies, we are studying the functional relationships among economically important traits using the statistical and computational approaches developed in our project to model the network involving those traits. Lastly, with the quail data, we are using network approaches to develop a statistical model to predict the total egg production of individual birds based on earlier expressed phenotypes such as growth and reproductive traits

Publications

  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Bouwman, A. C., Valente, B. D., Janss, L. L. G., Bovenhuis, H. and Rosa, G. J. M. Exploring causal networks of bovine milk fatty acids in multivariate mixed model context. Genetics Selection Evolution 46:2, 2014.
  • Type: Book Chapters Status: Published Year Published: 2014 Citation: Rosa, G. J. M. and Valente, B. D. Structural Equation Models for Studying Causal Phenotype Networks in Quantitative Genetics. In: Probabilistic Graphical Models for Genetics, Genomics and Postgenomics. Sinoquet, C. and Mourad, R. (Eds.) Oxford University Press, 2014.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2014 Citation: Valente. B. D, Morota, G., Rosa, G. J. M., Gianola, D. and Weigel, K. A. Causal meaning of genomic predictors: implication on genome-enabled selection modeling. In: 10th World Congress on Genetics Applied to Livestock Production, Vancouver, Canada, Aug. 1722, 2014 (https://asas.org/wcgalp-proceedings)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2014 Citation: Pe�agaricano, F., Valente, B. D., Steibel, J. P., Bates, R. O., Ernst, C. W., Khatib, H. and Rosa, G. J. M. Searching for causal networks involving latent variables in complex traits: An application to growth, carcass, and meat quality traits in pig. In: 10th World Congress on Genetics Applied to Livestock Production, Vancouver, Canada, Aug. 1722, 2014 (https://asas.org/wcgalp-proceedings)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2014 Citation: Felipe, V. P. S., Silva, M. A., Valente, B. D. and Rosa, G. J. M. Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails. In: 10th World Congress on Genetics Applied to Livestock Production, Vancouver, Canada, Aug. 1722, 2014 (https://asas.org/wcgalp-proceedings)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2014 Citation: Inoue, K., Valente, B. D, Shoji, N., Honda, T., Oyama, K. and Rosa, G. J. M. Searching for phenotypic causal links among meat quality traits in Japanese black cattle. In: 10th World Congress on Genetics Applied to Livestock Production, Vancouver, Canada, Aug. 1722, 2014 (https://asas.org/wcgalp-proceedings)


Progress 03/15/13 to 03/14/14

Outputs
Target Audience: This project is essentially of basic knowledge development and as such its target audience refers to individuals from the scientific community and industry who develop research. However, the applications of the methods developed in this project will allow applied researchers from various disciplines of animal sciences to better understand causal relationships between phenotypic traits such that better informed decisions regarding management interventions (e.g. improvement in diet, housing, reproductive program, etc.) will be possible, targeting the optimization of resources utilization and sustainability of livestock enterprises. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? The project has a PhD student (Mr. Francisco Penagaricano) and a post-doctoral fellow (Dr. Bruno Valente), who are directly involved with and supported by this project. In addition, the project has attracted international visiting scholars to the University of Wisconsin-Madison: Mrs. Aniek Bowman, from the Wageningen University, Netherlands; Mr. Raphael Rocha, from the Federal University of Minas Gerais, Brazil; Mr. Rodrigo Pacheck, from the Federal University of Vicosa, Brazil; and Mr. Pedro Cerqueira, from the University of Sao Paulo, Brazil. These students however were supported by their own institutions. How have the results been disseminated to communities of interest? The project results have been disseminated to communities of interest via peer-reviewed scientific publications, talks at scientific meeting and seminars both in the US and abroad. What do you plan to do during the next reporting period to accomplish the goals? For the next period we will apply the developed methods and software for the analysis of various datasets related to multiple traits in beef cattle, pigs and poultry.

Impacts
What was accomplished under these goals? During the third year of this project, we finalized the R codes for causal inference using the IC algorithm and structural equation model fitting. A step-by-step description of the code and its implementation has been published in the form of a book chapter in Valente and Rosa (2013). We also developed research on the advantages and disadvantages of structural equation modeling for the genetic improvement of multiple traits in livestock. This research has been published in the paper Valente et al. (2013). Other outputs of the project during this period refer to the presentation of seminars in different institutions: 1) "Causal Graphical Models in Quantitative Genetics and Genomics", Department of Biostatistics, Sao Paulo State University (UNESP), Botucatu - SP, Brazil, in June 21, 2013; 2) "Causal Graphical Models in Quantitative Genetics and Genomics", University of Florida, Gainesville, FL, in January 23, 2013; and 3) "Causal Graphical Models in Quantitative Genetics and Genomics", Department of Animal Sciences, University of Wisconsin-Madison, WI, in January 29, 2013.

Publications

  • Type: Book Chapters Status: Published Year Published: 2013 Citation: Valente, B. D. and Rosa, G. J. M. Mixed Effects Structural Equation Models and Phenotypic Causal Networks. In: Genome-Wide Association Studies. Gondro, C., van der Werf, J. and Hayes, B. (Eds.) Springer, 2013.
  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Valente, B. D., Rosa, G. J. M., Gianola, D., Wu, X.-L. and Weigel, K. A. Is structural equation modeling advantageous for the genetic improvement of multiple traits? Genetics 194: 561-572, 2013.
  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Rosa, G. J. M. and Valente B. D. Inferring causal effects from observational data in livestock. Journal of Animal Science 91: 553-564, 2013.
  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Valente, B. D., Morota, G., Pe�agaricano, F., Rosa, G. J. M., Gianola, D. and Weigel, K. A. The causal meaning of genomic predictors and how it affects construction and comparison of genome-enabled selection models. arXiv:1401.1165 (http://arxiv.org/abs/1401.1165), 2013.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2013 Citation: Valente B. D., Rosa, G. J. M., Gianola, D., and Weigel, K. A. Using identifiability of genetic causal effects as a criterion for covariate choice in genome-enabled selection models. In: ADSA-ASAS Joint Meeting, Indianapolis-IN, July 8-12, 2013.


Progress 03/15/12 to 03/14/13

Outputs
Target Audience: This project is essentially of basic knowledge development and as such its target audience refers to individuals from the scientific community and industry who develop research. However, the applications of the methods developed in this project will allow applied researchers from various disciplines of animal sciences to better understand causal relationships between phenotypic traits such that better informed decisions regarding management interventions (e.g. improvement in diet, housing, reproductive program, etc.) will be possible, targeting the optimization of resources utilization and sustainability of livestock enterprises. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? The project has a PhD student (Mr. Francisco Penagaricano) and a post-doctoral fellow (Dr. Bruno Valente), who are directly involved with and supported by this project. In addition, the project has attracted international visiting scholars to the University of Wisconsin-Madison: Mrs. Aniek Bowman, from the Wageningen University, Netherlands; and Mr. Raphael Rocha, from the Federal University of Minas Gerais, Brazil. These students however are supported by their own institutions. How have the results been disseminated to communities of interest? The project results have been disseminated to communities of interest via peer-reviewed scientific publications, talks at scientific meeting and seminars both in the US and abroad. What do you plan to do during the next reporting period to accomplish the goals? For the next period we will continue to improve the R code developed for structural model applications and start studying the utilization of genomic information for causal network inferences.

Impacts
What was accomplished under these goals? During the second year of this project, we developed extensive statistical analysis of data on bovine milk fatty acids. Some preliminary results have been presented in a scientific meeting and a manuscript for publication is on the works. We also developed a conceptual comparison between the standard multiple-trait and structural equation models for animal breeding applications. Other outputs of the project during this period refer to participation and presentation of invited talks in scientific meetings. The presentations directly related to this project were: 1) "Causal Graphical Models in Quantitative Genetics and Genomics", which was presented at the 2012 ADSA-AMPA-ASAS-CSAS-WSASAS Joint Annual Meeting (JAM). Phoenix, Arizona, July 15-19, 2012; and 2) "Inferring Causal Phenotype Networks Using Structural Equation Models", presented at the 57th Annual Meeting of the Brazilian Region (RBRAS), Piracicaba - Brazil, May 5-9, 2012. In addition, an international seminar was presented at the Kyoto University, in Japan, on August 31, 2012, with the title "Inferring Causal Phenotype Networks Using Structural Equation Models."

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2012 Citation: Bouwman, A., Valente, B. D., Bovenhuis, H. and Rosa, G. J. M. Structural equation models to study causal relationships between bovine milk fatty acids. In: 63rd EAAP, Bratislava, Slovakia, August 27-31, 2012.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2012 Citation: Rosa, G. J. M. and Valente, B. D. Causal graphical models in quantitative genetics and genomics settings. J. Anim. Sci. Vol. 90, Suppl. 3/J. Dairy Sci. Vol. 95, Suppl. 2: 58, 2012.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2012 Citation: Valente, B. D., Rosa, G. J. M., Wu, X.-L., Gianola, D. and Weigel, K. A. Conceptual comparison between standard multiple-trait and structural equation models in animal breeding applications. J. Anim. Sci. Vol. 90, Suppl. 3/J. Dairy Sci. Vol. 95, Suppl. 2: 459, 2012.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2012 Citation: Rosa, G. J. M. Inferring causal phenotype networks using structural equation models. In: 57th Annual Meeting of the Brazilian Region (RBRAS), Piracicaba - Brazil, May 5-9, 2012.


Progress 03/15/11 to 03/14/12

Outputs
OUTPUTS: During the first year of this project, we developed extensive statistical data analysis of a dataset on reproductive traits in quail, with the objective of inferring causal links between the phenotypic traits. Software written in R language was also developed to search for plausible causal structures, which will be freely available to the scientific community after more testing and debugging is conducted. Other outputs of the project during this period refer to participation and presentation of invited talks in scientific meetings. The presentations directly related to this project were: 1) "Inferring Causal Phenotype Networks Using Structural Equation Models: Applications in Molecular and Quantitative Genetics", which was presented as the 2011 LeClerg Rotary Lecture at the University of Maryland, MD, on April 7, 2011; and 2) "Phenotype Network Reconstruction Using Genomic Information", which was delivered at the 6th International Conference on Genomics, in Shenzhen - China, November 12-15, 2011. In addition, an international seminar was presented at the Department of Animal Sciences, Wageningen University, Wageningen, The Netherlands, on April 29, 2011, with title "Inferring causal phenotype networks using structural equation models: applications in molecular and quantitative genetics." Finally, a grad student (Mr. Francisco Penagaricano) and a post-doctoral fellow (Dr. Bruno Valente) are directly involved with and supported by this project, and other two visiting scholars at the University of Wisconsin (Mrs. Aniek Bowman - Wageningen University, Netherlands, and Mr. Raphael Rocha, Federal University of Minas Gerais, Brazil) have also been working on this project, and supported by their mother institutions, all contributing to the mentoring and training component of the project. PARTICIPANTS: The principal investigator(s)/project director(s) of the project are Drs. Guilherme J. M. Rosa (PI), Nick Wu, Daniel Gianola and Kent Weigel (Co-PDs). In addition, the project has a PhD student (Mr. Francisco Penagaricano) and a post-doctoral fellow (Dr. Bruno Valente), who are directly involved with and supported by this project. In addition, the project has attracted two visiting scholars to the University of Wisconsin-Madison: Mrs. Aniek Bowman, from the Wageningen University, Netherlands; and Mr. Raphael Rocha, from the Federal University of Minas Gerais, Brazil. These two students however are supported by their own institutions. TARGET AUDIENCES: This project is essentially basic knowledge development and as such its target audience refers to individuals from the scientific community and industry who develop research. However, the applications of the methods developed in this project will allow applied researchers from various disciplines of animal sciences to better understand causal relationships between phenotypic traits such that more informed decisions regarding management interventions (regarding improvement in diet, housing, reproductive program, etc.) will be possible, targeting the optimization of resources utilization. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
With the completion of the first objective of the project during its first year, we developed skills related to statistical testing and computational strategies for inferring phenotypic causal links using multi-trait measurements collected from a group of related individuals, which is the typical situation in livestock. Our work is unique in the sense that it allows accounting for genetic confounders when searching for causal relationships among phenotypic traits. The results obtained from such analyses when applied to economically important traits in livestock can then be used for a more informed decision-making regarding optimal management strategies in farm animals.

Publications

  • Valente, B. D., Rosa, G. J. M., Teixeira, R. B. and Torres, R. A. Searching for phenotypic causal networks involving complex traits: an application to European quails. Genet. Sel. Evol. 43:37, 2011.
  • Rosa, G. J. M., Valente, B. D., de los Campos, G., Wu, X.-L., Gianola, D. and Silva, M. A. Inferring causal phenotype networks using structural equation models. Genetics Selection Evolution 43: 6, 2011.
  • Valente, B. D., Rosa, G. J. M., Silva, M. A., Teixeira, R. B. and Torres, R. A. Searching for causal relationships among five traits of European quails. In: Joint ADSA/ASAS, New Orleans, LA, July 1-14, 2011.