Functional Genomics Complements Quantitative Genetics in Identifying Disease-Gene Associations

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.

[1]  Matthew A. Hibbs,et al.  Discovery of biological networks from diverse functional genomic data , 2005, Genome Biology.

[2]  Judith A. Blake,et al.  The Mouse Genome Database (MGD): from genes to mice—a community resource for mouse biology , 2004, Nucleic Acids Res..

[3]  Adam J. Smith,et al.  The Database of Interacting Proteins: 2004 update , 2004, Nucleic Acids Res..

[4]  Yurii S. Aulchenko,et al.  Twenty bone mineral density loci identified by large-scale meta-analysis of genome-wide association studies , 2009, Nature Genetics.

[5]  T. Barrette,et al.  Probabilistic model of the human protein-protein interaction network , 2005, Nature Biotechnology.

[6]  Yin Tintut,et al.  Hyperlipidemia Impairs Osteoanabolic Effects of PTH , 2008, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[7]  Scott M. Williams,et al.  challenges for genome-wide association studies , 2010 .

[8]  M. Tyers,et al.  The GRID: The General Repository for Interaction Datasets , 2003, Genome Biology.

[9]  Jason H. Moore,et al.  BIOINFORMATICS REVIEW , 2005 .

[10]  T. Ideker,et al.  Supporting Online Material for A Systems Approach to Mapping DNA Damage Response Pathways , 2006 .

[11]  A. Owen,et al.  A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae) , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[12]  O. Troyanskaya,et al.  Predicting gene function in a hierarchical context with an ensemble of classifiers , 2008, Genome Biology.

[13]  M. McCarthy,et al.  Genome-wide association studies for complex traits: consensus, uncertainty and challenges , 2008, Nature Reviews Genetics.

[14]  G. Churchill,et al.  Femur Mechanical Properties in the F2 Progeny of an NZB/B1NJ × RF/J Cross Are Regulated Predominantly by Genetic Loci That Regulate Bone Geometry , 2006, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[15]  G. Sumara,et al.  A Probabilistic Functional Network of Yeast Genes , 2004 .

[16]  Paul T Tarr,et al.  Emerging new paradigms for ABCG transporters. , 2009, Biochimica et biophysica acta.

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Bart De Moor,et al.  BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis , 2005, Bioinform..

[19]  Matthew A. Hibbs,et al.  Exploring the human genome with functional maps. , 2009, Genome research.

[20]  Robert W. Williams,et al.  Methodological aspects of the genetic dissection of gene expression , 2005, Bioinform..

[21]  E. Marcotte,et al.  Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana , 2010, Nature Biotechnology.

[22]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[23]  D. Clayton,et al.  Genome-wide association studies: theoretical and practical concerns , 2005, Nature Reviews Genetics.

[24]  Olga G. Troyanskaya,et al.  Global Prediction of Tissue-Specific Gene Expression and Context-Dependent Gene Networks in Caenorhabditis elegans , 2009, PLoS Comput. Biol..

[25]  S. Batalov,et al.  A gene atlas of the mouse and human protein-encoding transcriptomes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[26]  John D. Storey,et al.  Genetic interactions between polymorphisms that affect gene expression in yeast , 2005, Nature.

[27]  Judith A. Blake,et al.  The Mouse Genome Database (MGD): mouse biology and model systems , 2007, Nucleic Acids Res..

[28]  Yuanfang Guan,et al.  A Genomewide Functional Network for the Laboratory Mouse , 2008, PLoS Comput. Biol..

[29]  Zhenjun Hu,et al.  Visant: an Integrative Framework for Networks in Systems Biology , 2008 .

[30]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[31]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[32]  Kriston L. McGary,et al.  Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes , 2007, Genome Biology.

[33]  Sarah Barber,et al.  A mouse atlas of gene expression: large-scale digital gene-expression profiles from precisely defined developing C57BL/6J mouse tissues and cells. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[34]  B. Frey,et al.  The functional landscape of mouse gene expression , 2004, Journal of biology.

[35]  E. Stone,et al.  The genetics of quantitative traits: challenges and prospects , 2009, Nature Reviews Genetics.

[36]  P. Vestergaard,et al.  Regional and Hormone‐Dependent Effects of Apolipoprotein E Genotype on Changes in Bone Mineral in Perimenopausal Women * , 2001, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[37]  G. Churchill,et al.  Strain-specific effects of rosiglitazone on bone mass, body composition, and serum insulin-like growth factor-I. , 2009, Endocrinology.

[38]  Keiichi Sasaki,et al.  Osteoblasts and osteocytes express MMP2 and -8 and TIMP1, -2, and -3 along with extracellular matrix molecules during appositional bone formation. , 2004, The anatomical record. Part A, Discoveries in molecular, cellular, and evolutionary biology.

[39]  Cynthia L. Smith,et al.  The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information , 2004, Genome Biology.

[40]  István Takács,et al.  Single nucleotide polymorphisms in new candidate genes are associated with bone mineral density and fracture risk. , 2008, European journal of endocrinology.

[41]  Yi-Hsiang Hsu,et al.  Mouse BMD Quantitative Trait Loci Show Improved Concordance With Human Genome-wide Association Loci When Recalculated on a New, Common Mouse Genetic Map , 2010, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[42]  Ian M. Donaldson,et al.  The Biomolecular Interaction Network Database and related tools 2005 update , 2004, Nucleic Acids Res..

[43]  L. Liang,et al.  Mapping complex disease traits with global gene expression , 2009, Nature Reviews Genetics.

[44]  A. Fraser,et al.  A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans , 2008, Nature Genetics.

[45]  Dong Dong,et al.  IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model , 2006, BMC Bioinformatics.

[46]  Samuel Varghese,et al.  Matrix metalloproteinases and their inhibitors in bone: an overview of regulation and functions. , 2006, Frontiers in bioscience : a journal and virtual library.

[47]  Igor Jurisica,et al.  Online Predicted Human Interaction Database , 2005, Bioinform..

[48]  J. Kaufman,et al.  Prevalent fractures are related to cortical bone geometry in young healthy men at age of peak bone mass , 2010, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[49]  Erik L. L. Sonnhammer,et al.  Inparanoid: a comprehensive database of eukaryotic orthologs , 2004, Nucleic Acids Res..

[50]  Wenjiang J. Fu,et al.  Estimating misclassification error with small samples via bootstrap cross-validation , 2005, Bioinform..

[51]  E. Snitkin,et al.  Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network , 2009, Genome Biology.