STATISTICAL METHODS FOR INFERRING GENETIC REGULATION ACROSS HETEROGENEOUS SAMPLES AND MULTIMODAL DATA

Abstract

As clinical datasets have increased in size and a wider range of molecular profiles can be credibly measured, understanding sources of heterogeneity has become critical in studying complex phenotypes. Here, we investigate and develop statistical approaches to address and analyze technical variation, genetic diversity, and tissue heterogeneity in large biological datasets. Commercially available methods for normalization of NanoString nCounter RNA expression data are suboptimal in fully addressing unwanted technical variation. First, we develop a more comprehensive quality control, normalization, and validation framework for nCounter data, benchmark it against existing normalization methods for nCounter, and show its advantages on four datasets of differing sample sizes. We then develop race-specific and genetic ancestry-adjusted tumor transcriptomic prediction models from germline genetics in the Carolina Breast Cancer Study (CBCS) and study the performance of these models across ancestral groups and molecular subtypes. These models are employed in a transcriptome-wide association study (TWAS) to identify four novel genetic loci associated with breast-cancer specific survival. Next, we extend TWAS to a novel suite of tools, MOSTWAS, to prioritize distal genetic variation in transcriptomic predictive models with two multi-omic approaches that draw from mediation analysis. We empirically show the utility of these extensions in simulation analyses, TCGA breast cancer data, and ROS/MAP brain tissue data. We develop a novel distal-SNPs added-last test, to be used with MOSTWAS models, to prioritize distal loci that give added information, beyond the association in the local locus around a gene. Lastly, we develop DeCompress, a deconvolution method from gene expression from targeted RNA panels such as NanoString, which have a much smaller feature space than traditional RNA expression assays. We propose an ensemble approach that leverages compressed sensing to expand the feature space and validate it on data from the CBCS. We conduct extensive benchmarking of existing deconvolution methods using simulated in-silico experiments, pseudo-targeted panels from published mixing experiments, and data from the CBCS to show the advantage of DeCompress over reference-free methods. We lastly show the utility of in-silico cell-type proportion estimation in outcome prediction and eQTL mapping.Doctor of Philosoph