Genome Wide Association Studies (GWAS) have uncovered an unprecedented number of variants associated with important health-related traits and diseases. Evidence from these studies suggests that most clinically relevant traits have complex genetic architectures. Whole Genome Prediction (WGP) is a predictive approach, primarily developed and tested in the field of animal breeding, designed to confront some of the challenges emerging in the prediction of complex traits and diseases. Implementing WGP requires specialized software, which is not available in standard statistical packages. In our research projects involving plant, animal and more recently human data, we have developed, tested and used statistical software for parametric and semi-parametric WGP. In this project we propose to integrate and further develop this software in ways that will improve its value for applications with human data. We will integrate parametric and semi-parametric procedures for WGP into a unified framework and will deliver software that could be used with un-censored, censored, binary and ordinal traits. The software produced in this project will be delivered as an R-package and will be integrated into GenePattern;a bioinformatics platform where users will be able to develop analysis pipelines by combining our software with other bioinformatics tools.
Genome Wide Association Studies (GWAS) have uncovered an unprecedented number of variants associated with important health-related traits and diseases. Evidence from these studies suggests that most clinically relevant traits have complex genetic architectures. Whole Genome Prediction (WGP) is a predictive approach, primarily developed and tested in the field of animal breeding, designed to confront some of the challenges emerging in the prediction of complex traits and diseases. We believe that this methodology offers great opportunities to advance our ability to predict genetic predisposition to complex human traits and diseases. Implementing WGP methods requires specialized software, which is not available in standard statistical packages. In our research we have developed, tested, and used statistical software for parametric and non-parametric WGP. The proposed project will integrate these software into a unified framework, will further develop these packages by implementing additional regression methods, and will extend the software to handle traits often encountered in human applications such as censored, binary and ordinal outcomes. The software developed in this project will be integrated into R and into GenePattern, a bioinformatics workflow platform which will enable users to integrate our software with other bioinformatics tools.
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| Enciso-Rodriguez, Felix; Douches, David; Lopez-Cruz, Marco et al. (2018) Genomic Selection for Late Blight and Common Scab Resistance in Tetraploid Potato (Solanum tuberosum). G3 (Bethesda) 8:2471-2481 |
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| Sun, Mengying; Vazquez, Ana I; Reynolds, Richard J et al. (2018) Untangling the complex relationships between incident gout risk, serum urate, and its comorbidities. Arthritis Res Ther 20:90 |
| de Los Campos, Gustavo; Vazquez, Ana Ines; Hsu, Stephen et al. (2018) Complex-Trait Prediction in the Era of Big Data. Trends Genet 34:746-754 |
| Lello, Louis; Avery, Steven G; Tellier, Laurent et al. (2018) Accurate Genomic Prediction of Human Height. Genetics 210:477-497 |
| Kim, Hwasoon; Grueneberg, Alexander; Vazquez, Ana I et al. (2017) Will Big Data Close the Missing Heritability Gap? Genetics 207:1135-1145 |
| Pickens, C Austin; Vazquez, Ana I; Jones, A Daniel et al. (2017) Obesity, adipokines, and C-peptide are associated with distinct plasma phospholipid profiles in adult males, an untargeted lipidomic approach. Sci Rep 7:6335 |
| Pérez-Enciso, M; de Los Campos, G; Hudson, N et al. (2017) The 'heritability' of domestication and its functional partitioning in the pig. Heredity (Edinb) 118:160-168 |
| González-Reymúndez, Agustín; de Los Campos, Gustavo; Gutiérrez, Lucía et al. (2017) Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions. Eur J Hum Genet 25:538-544 |
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