Leveraging Distribution Quantiles to Detect Gene Interactions in the Pursuit of Personalized Medicine

Abstract

Anticipations of personalized medicine are primarily attributed to the recent advances in computational science and high-throughput technologies that enable the ever-more realistic modeling of complex diseases. These diseases result from the interplay between genes and environment that have limited our ability to predict, prevent, or treat them. While many envision the utility of integrated high-dimensional patient-specific information, basic research towards developing accurate and reliable frameworks for personalized medicine is relatively slow in progress. This thesis provides a state-of-the-art review of current challenges towards personalized medicine. There is a need for global investment in basic research that includes 1) cost-effective generation of high-quality high-throughput data, 2) hybrid education and multidisciplinary teams, 3) data storage and processing, 4) data integration and interpretation, and 5) individual and global economic relevance; to be followed by global investments into public health to adopt routine personalized medicine. This review also highlights that unknown or unadjusted interactions result in true heterogeneity in the effect and relevance of patient data. This limits our ability to integrate and reliably utilize high-dimensional patient-specific data. This thesis further investigates the true heterogeneity in marginal effects of known BMI genetic variants. This involved the development of the novel statistical method, meta-quantile regression (MQR), to identify variants with potential gene-gene / gene-environment interactions. Applying MQR on public and local data (75,230 European adults) showed that FTO, PCSK1, TCF7L2, MC4R, FANCL, GIPR, MAP2K5, and NT5C2 have potential interactions on BMI. In addition, a gene score of 37 BMI variants shows that the genetic architecture of BMI is shaped by gene-gene and gene-environment interactions. The computational cost of fitting MQR models was greatly reduced using unconditional quantile regression. The utility of MQR was further compared to variance heterogeneity tests in identifying variants with potential interactions. MQR tests were found to have a higher power of detecting synergetic and antagonistic interactions for skewed quantitative traits while maintaining nominal Type I error rates compared to variance heterogeneity tests. Overall, MQR is a valuable tool to detect potential interactions without imposing assumptions on the nature of interactions.