Scalable approaches for analysis of human genome-wide ...

7Characterising the Genetic Control of HumanMetabolic GenesIn previous chapters we explored the use of gene expression data for prediction of breastcancer metastasis and of genetic data for predicting complex disease status. However, consideringthe gene expression and genetic aspects in isolation provides only narrow insight intothe underlying biological mechanisms of disease. An integrated analysis of several data typescan potentially provide better understanding of the hidden relationships between the cellularcomponents and biological processes, and the links between these processes and the observedclinical phenotypes. In this chapter, we perform an integrative analysis of SNPs, gene expression,and metabonomic data from a human population cohort, based on the sparse linearmodels discussed earlier.7.1. IntroductionThe availability of gene expression, genetic, and metabonomic datasets has allowed integratedanalyses of the relationships between genetic variation and gene expression (Mackayet al., 2009; Stranger et al., 2007) and between genetic variation and metabolites such asblood lipids (Ferrara et al., 2008; Inouye et al., 2010a,b; Surakka et al., 2011; Teslovich etal., 2010; Tukiainen et al., 2011). These datasets can be further integrated with high-levelclinical phenotypes such as occurrence of disease (Holmes et al., 2008; Nicholson and Lindon,2008). While such studies have produced valuable insights into the regulatory mechanisms141