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 #
1R01GM101219-01
Application #
8274041
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
2012-03-01
Budget End
2013-01-31
Support Year
1
Fiscal Year
2012
Total Cost
$241,145
Indirect Cost
$71,145
Name
University of Alabama Birmingham
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
063690705
City
Birmingham
State
AL
Country
United States
Zip Code
35294
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
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
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
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
Vazquez, Ana I; Veturi, Yogasudha; Behring, Michael et al. (2016) Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles. Genetics 203:1425-38
de Los Campos, Gustavo; Sorensen, Daniel; Gianola, Daniel (2015) Genomic heritability: what is it? PLoS Genet 11:e1005048
Vazquez, Ana I; Klimentidis, Yann C; Dhurandhar, Emily J et al. (2015) Assessment of whole-genome regression for type II diabetes. PLoS One 10:e0123818
de Los Campos, Gustavo; Veturi, Yogasudha; Vazquez, Ana I et al. (2015) Incorporating Genetic Heterogeneity in Whole-Genome Regressions Using Interactions. J Agric Biol Environ Stat 20:467-490
Lian, Lian; de Los Campos, Gustavo (2015) FW: An R Package for Finlay-Wilkinson Regression that Incorporates Genomic/Pedigree Information and Covariance Structures Between Environments. G3 (Bethesda) 6:589-97

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