The completion of the human genome project brought the promise of genomic medicine-the use of genomic information for prevention, diagnosis and treatment of diseases. Yet, despite great progress in genotyping technologies, our ability to predict genetic predisposition to complex human traits and diseases remains very limited. Part of the explanation of our paradoxical lack of ability to predict complex human traits may reside in the limitations posed by the statistical methods commonly used in genome wide association studies. We believe that alternative methods, largely adapted from the field of animal breeding (WGP, whole-genome prediction), can enhance our ability to predict complex human traits and diseases, thus paving the way towards more intensive use of genomic information in personalized medicine. However, the populations to which WGP has been successfully applied differ greatly from human populations in aspects such as selection history, distribution of allele frequency, extent linkage disequilibrium (LD) and inbreeding. And preliminary evidence indicates that these factors can impact the predictive performance of WGP. Therefore, a comprehensive evaluation of WGP with human data is needed, and new methods may need to be developed to cope with the challenges posed by the prediction of complex human traits. We propose a framework to study the factors affecting the ability of WGP to account for and to predict variance at un-observed QTL. Using this framework, and a combination of simulation and real data analysis, we will produce the first comprehensive evaluation of existing WGP with human data and will quantify the effects of key features of the data, of the trait of interest, and of the regression method on the prediction accuracy of existing WGP procedures. We will use this information to develop new methods designed to confront the limitations of existing ones.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
7R01GM099992-04
Application #
9060460
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Krasnewich, Donna M
Project Start
2012-09-01
Project End
2017-06-30
Budget Start
2015-07-01
Budget End
2017-06-30
Support Year
4
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Michigan State University
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
193247145
City
East Lansing
State
MI
Country
United States
Zip Code
48824
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
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, 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
Sun, Xiaochen; Fernando, Rohan; Dekkers, Jack (2016) Contributions of linkage disequilibrium and co-segregation information to the accuracy of genomic prediction. Genet Sel Evol 48:77
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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