Gene expression is a fundamental function of any cell. It is the main mechanism by which information is transmitted from the nucleus to the rest of the cell and eventually to other cells and the body of the organism. Genetic variation in components of the transcriptional machinery and signals that regulate gene function generates variation in transcriptional response and consequently variation in phenotypes. It has become apparent that many of the common genetic signals associated with disease are found away from the DNA sequence that encodes for protein sequence and is likely to be functioning in regulating gene expression. In this project we propose to develop methodologies that will explore the consequences of genetic variation in gene expression. There are three main goals of this project. First we will explore and develop methodologies to mine information from experiments that perform deep sequencing of the human transcriptome. The new sequencing technologies are providing us with unprecedented resolution into the transcriptome but are also raising challenges in the computational and biological models to use to interpret such large amounts of data. Secondly, we will use high-resolution genetic data to develop and use methodologies to dissect the fine structure of genetic variants that affect regulation of gene expression. Finally, we will implement and test models to infer the higher-order interactions of genome function so as to dig deeply into the biological consequences of genetic variants and how the signal is transmitted from the DNA sequence to higher levels of cell and body function. Our goals is to develop methodologies that will significantly improve our insight to the variability in human populations and assist in interpreting predisposition to genetic diseases.

Public Health Relevance

Project narrative The proposed project aims at the development of statistical methods for the interpretation and study of the impact of genetic variants in cell function. Understanding the cellular effects of genetic variants provides a fundamental framework for the deep understanding of human genetic disease and increases the potential for the development of relevant treatments and drugs. It is the understanding of the basic molecular functions in health and disease that will provide the utmost resolution of information for the improvement of human health.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1-GGG-A (52))
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Bender, Patrick
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University of Geneva
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Chiang, Colby; Scott, Alexandra J; Davis, Joe R et al. (2017) The impact of structural variation on human gene expression. Nat Genet 49:692-699
Mohammadi, Pejman; Castel, Stephane E; Brown, Andrew A et al. (2017) Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change. Genome Res 27:1872-1884
Tan, Meng How; Li, Qin; Shanmugam, Raghuvaran et al. (2017) Dynamic landscape and regulation of RNA editing in mammals. Nature 550:249-254
Dolan, M Eileen; El Charif, Omar; Wheeler, Heather E et al. (2017) Clinical and Genome-Wide Analysis of Cisplatin-Induced Peripheral Neuropathy in Survivors of Adult-Onset Cancer. Clin Cancer Res 23:5757-5768
Yang, Fan; Wang, Jiebiao; GTEx Consortium et al. (2017) Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis. Genome Res 27:1859-1871
Agrawal, A; Chou, Y-L; Carey, C E et al. (2017) Genome-wide association study identifies a novel locus for cannabis dependence. Mol Psychiatry :
Gudmundsson, Julius; Thorleifsson, Gudmar; Sigurdsson, Jon K et al. (2017) A genome-wide association study yields five novel thyroid cancer risk loci. Nat Commun 8:14517
Bai, Xue; Mangum, Kevin D; Dee, Rachel A et al. (2017) Blood pressure-associated polymorphism controls ARHGAP42 expression via serum response factor DNA binding. J Clin Invest 127:670-680
Mercader, Josep M; Liao, Rachel G; Bell, Avery D et al. (2017) A Loss-of-Function Splice Acceptor Variant in IGF2 Is Protective for Type 2 Diabetes. Diabetes 66:2903-2914
Peckham-Gregory, Erin C; Chakraborty, Rikhia; Scheurer, Michael E et al. (2017) A genome-wide association study of LCH identifies a variant in SMAD6 associated with susceptibility. Blood 130:2229-2232

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