Assessing the impact of genetic variants on cellular phenotypes like gene expression provide new opportunities for understanding the biology of genomes and disease. By identifying expression and splicing quantitative trait loci (eQTL or sQTL), we can elucidate new mechanisms underlying trait-associated variation and gain new insights into gene regulatory mechanisms and pathways. With the availability of novel technologies and new large datasets we are now in a position to perform high resolution analysis of transcriptomes and elucidate causal cellular mechanisms for phenotypic variability and disease. In the proposed project we aim to do the following:
Specific Aim 1 : We will undertake detailed transcriptome analysis of the GTEx data. We will improve the workflow of transcriptome analysis by deploying novel computational methods. First, we will tackle the problem of identifying transcripts and estimating their abundances. Second, we will deploy a Bayesian approach for comparing transcript distribution within and among populations and tissues to develop a robust catalog of differentially expressed genes.
Specific Aim 2 : We will develop improved statistical methods to discover regulatory variation. Over the past 3-4 years, our collaborative group has developed many tools for mapping genetic variants underlying expression differences among individuals. Here, we will apply these tools to the GTEx data to map eQTL using the high-quality transcriptome feature quantifications from Aim 1. The approaches we will deploy include: (i) haplotype-based methods for mapping of cis eQTL, (ii) improved methods for quantifying Allele Specific Expression (ASE), (iii) Bayesian mapping of trans eQTL using GRNs, and (iv) integrated multi-tissue and multi-population eQTL mapping.
Specific Aim 3 : We will map putatively causal variants that affect gene expression or transcript structure and assess their functional attributes. To understand the molecular bases of human gene regulation, we will create a comprehensive catalog of causal variants influencing expression and their associated genomic features. We will focus on: (i) the study of patterns of chromatin states to define rules for the location and effect of eQTL;and (ii) the interpretation o loss of function variant effects on transcriptomes and individuals.
Specific Aim 4 : We will build quantitative genetic and gene regulatory models of cellular transcript abundance. Our main efforts under this aim will be: (i) to assess patterns of epistasis/penetrance between protein-coding and regulatory variation;and (ii) reconstruct gene regulatory networks. These models will provide biological insights into the causes and consequences of eQTL.

Public Health Relevance

Assessing the impact of genetic variation on gene expression provide new opportunities for understanding the biology of genomes and disease. With the availability of novel technologies and new large datasets we are now in a position to perform high-resolution analysis of gene expression and elucidate causal mechanisms for phenotypic variability and disease. This will likely bring new opportunities for diagnostic and prognostic methodologies and a handle on personalized medicine.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1-GGG-H (50))
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Addington, Anjene M
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University of Geneva
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Monlong, Jean; Calvo, Miquel; Ferreira, Pedro G et al. (2014) Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nat Commun 5:4698
Battle, A; Montgomery, S B (2014) Determining causality and consequence of expression quantitative trait loci. Hum Genet 133:727-35