Abstract
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.
Highlights
Understanding the genetic bases of human disease has been an overarching goal of biology since the foundation of genetics as a scientific discipline
We develop a new algorithm that accurately predicts the phenotypic effects of genetic perturbations based on functional genomic data, and we demonstrate that this approach complements the results of quantitative genetics studies
Neither of these genes is a candidate from any previous quantitative genetics study of bone mineral density (BMD), which indicates that our approach produces results that are complementary to genome-wide association studies (GWAS) and quantitative trait loci (QTL) studies
Summary
Understanding the genetic bases of human disease has been an overarching goal of biology since the foundation of genetics as a scientific discipline. Each of these approaches for quantitative genetics have common and unique unresolved issues that limits their utility. Both QTL and GWAS approaches can suffer from sampling biases. Many linkage mapping QTL studies lack the statistical power to narrowly define a causal loci, often resulting in regions spanning entire chromosomes that contain hundreds of candidate genes [5]. While these QTL regions are often broad, they can typically explain a large fraction of phenotypic variation. A meta-analysis GWAS of bone mineral density (BMD) based on nearly 20,000 genotyped and phenotyped individuals can only account for less than 3% of the observed heritability of BMD [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
