Thousands of genome-wide association studies link specific diseases or complex phenotypes to single mutations in the human genome. But translating these results to medical treatments requires a precise understanding of how that mutation contributes to the mechanism of disease. Currently, the regulatory role of single nucleotide polymorphisms (SNPs) is, for the most part, confined to local, or cis-, expression quantitative trait loci (eQTLs) in a small number of human tissues. But not all diseases or complex phenotypes are mediated by cis-eQTLs. Very few long-distance, or trans-, eQTLs have been identified and validated in human tissues, although trans-eQTLs play an important role in some complex phenotypes. Alternative splicing has also been shown to modulate certain phenotypes;however, little is known about SNPs that regulate alternative splicing. The proposed K99/R00 research seeks to design statistical methods that build gene and transcript networks to identify SNPs that regulate gene and mRNA isoform transcription, both locally and over long distances, and to validate those findings, for the purpose of providing insight into mechanisms for complex phenotypes and disease. We propose to leverage cis-eQTLs and gene expression data in humans identified in our current work to build precise, directed gene networks on a genome-scale. We will build these networks using Bayesian statistical models to compute the probability of a particular network with respect to each gene in the network jointly, with associated eQTLs providing information about whether regulated genes are upstream or downstream of other network genes. We will use Markov chain Monte Carlo and linear programming relaxation methods that have been shown to find near-optimal solutions to this type of problem. We will use these networks to identify trans-eQTLs, and quantify the effect of each trans-eQTL in a particular process using Bayesian statistical tests developed in our lab. Subsequently, we propose to exploit the opportunities of novel RNA sequencing techniques and nonparametric statistical models to identify transcript isoforms for each transcribed gene and, simultaneously, individual-specific transcript levels by extending sparse factor analysis models. This will enable us to identify QTLs that regulate the transcription of specific transcript isoforms (tQTLs) via alternative splicing events by extending the methods we have for eQTL identification. We will use the methodology we developed for eQTLs to build networks for transcript isoforms (transcript networks). Finally, we will use transcript networks to identify and quantify tQTLs that regulate individual-specific levels of transcript isoforms both locally and over long genetic distances, as with eQTLs. We will make all of our methods and results publicly available.
Thousands of genome-wide association studies link specific diseases or complex traits to single mutations in the human genome, but these results cannot yet be translated to medical treatments because knowing that a mutation is associated with a disease does not, in fact, give us insight into how that mutation contributes to the mechanism of disease. Our proposed research will design and validate statistical methods that provide a comprehensive road map to understanding the biological role of the mutations that are identified in these association studies. With the role of thousands of possibly disease-related mutations in hand, researchers can begin to piece together the mechanism of a disease and translate their findings into treatments for the disease much more quickly.
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