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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM101219-03
Application #
8607197
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2012-03-01
Project End
2015-01-31
Budget Start
2014-02-01
Budget End
2015-01-31
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Alabama Birmingham
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
City
Birmingham
State
AL
Country
United States
Zip Code
35294
Bernal Rubio, Yeni L; González-Reymúndez, Agustin; Wu, Kuan-Han H et al. (2018) Whole-Genome Multi-omic Study of Survival in Patients with Glioblastoma Multiforme. G3 (Bethesda) 8:3627-3636
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
Bellot, Pau; de Los Campos, Gustavo; Pérez-Enciso, Miguel (2018) Can Deep Learning Improve Genomic Prediction of Complex Human Traits? Genetics 210:809-819
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
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
Pérez-Enciso, Miguel; Forneris, Natalia; de Los Campos, Gustavo et al. (2017) Evaluating Sequence-Based Genomic Prediction with an Efficient New Simulator. Genetics 205:939-953
Kim, Hwasoon; Grueneberg, Alexander; Vazquez, Ana I et al. (2017) Will Big Data Close the Missing Heritability Gap? Genetics 207:1135-1145

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