|Title||Functional genomics complements quantitative genetics in identifying disease-gene associations.|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Guan, Y, Ackert-Bicknell, CL, Kell, B, Troyanskaya, OG, Hibbs, MA|
|Journal||PLoS Comput Biol|
|Keywords||Algorithms, Animals, Artificial Intelligence, ATP-Binding Cassette Transporters, Bayes Theorem, Bone Density, Chromosome Mapping, Cluster Analysis, Databases, Genetic, Disease Models, Animal, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Lipoproteins, Mice, Mice, Transgenic, Osteoporosis, Phenotype, Quantitative Trait Loci, Reproducibility of Results, Risk Factors, Tissue Inhibitor of Metalloproteinase-2|
An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype.
|Alternate Journal||PLoS Comput. Biol.|