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.
| Lello, Louis; Avery, Steven G; Tellier, Laurent et al. (2018) Accurate Genomic Prediction of Human Height. Genetics 210:477-497 |
| Bellot, Pau; de Los Campos, Gustavo; Pérez-Enciso, Miguel (2018) Can Deep Learning Improve Genomic Prediction of Complex Human Traits? Genetics 210:809-819 |
| Zeng, Jian; Garrick, Dorian; Dekkers, Jack et al. (2018) A nested mixture model for genomic prediction using whole-genome SNP genotypes. PLoS One 13:e0194683 |
| 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 |
| Kim, Hwasoon; Grueneberg, Alexander; Vazquez, Ana I et al. (2017) Will Big Data Close the Missing Heritability Gap? Genetics 207:1135-1145 |
| 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 |
| 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 |
Showing the most recent 10 out of 40 publications
