Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations

Abstract

To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models.

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations

“…Phenomics has a chance of changing how we view heritable diseases first by defining latent phenotypes that underlie genetically similar phenotype categories and second by revealing unexpected genetic links among disease entities. (75) The incorporation of genetic data clearly improves the quality of the predictions over those derived solely from trait correlations, (76) although phenotypic overlap is often a very good predictor of functional relatedness of the underlying genes. (77) Thus van Driel and colleagues (78) found that similarity between phenotypes did correlate positively with several measures of gene function, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein-protein interaction.…”

Correction for hidden confounders in the genetic analysis of gene expression

“…From a statistical perspective, one can use the results of an eQTL study to prioritize a list of disease-associated loci to follow up on; that is, one can use the existence of a SNP-gene-expression association as prior evidence that variation at the locus is more likely to have disease consequences (2,6). Furthermore, eQTL studies can infuse causal information into gene-gene and protein-protein correlation networks by making use of the fact that DNA can affect gene expression, but not the other way around (1,(7)(8)(9). Finally, the utility of eQTL studies is likely to increase as larger and more diverse datasets are amassed, and with the advent of new technologies such as RNA sequencing and exon arrays (2).…”

NEW: Network-Enabled Wisdom in Biology, Medicine, and Health Care

“…Bayesian network reconstruction (11, 12) is a powerful approach for simultaneously considering thousands of molecular or clinical variables and for identifying patterns of causal relationships between these variables in a completely data-driven fashion. We developed a way to overcome the chief limitation of this approach-deriving predictive models from correlation data (11,12,35)by leveraging DNA variation as a systematic source of perturbation (32). The resulting probabilistic causal networks are critical for understanding the behavior of any one gene in the context of human disease, because individual genes operate in molecular networks that define disease-associated biological and pathological events.…”