With the completion of the human genome project, much of the progress in understanding the genetic basis of disease relies on computational analysis of genomic data. Some of the most useful data for this analysis is human variation data. This data consists of information on the variation in genes associated with a disease for a population of individuals. Understanding the relation between variation and disease is a fundamental challenge, which can shed light on the genetic basis and mechanisms of human disease. This challenge spans three research fields: genetics, bioinformatics and medicine. Understanding the genetic basis of disease involves two steps. First, we must determine the functional variants in each gene locus that is linked to the disease and the effect of functional variants on the regulation and gene products of the gene. Second, we must understand how these intermediate phenotypes affect disease outcomes. Using this information, we can identify subtypes of the disease which are candidates for different drug response. In this proposal we outline our approach for this problem and propose to build tools for modeling the function of variation in a gene locus, correlating the intermediate phenotypes to disease outcomes and identifying subtypes of the disease based on genetic variants. The core of our approach involves haplotype analysis and we leverage previously developed tools for this analysis. We demonstrate initial results over the Chromogranin A locus. The disease focus of this proposal is hypertension and the tools will be applied to the large amount of data collected at UCSD through the pharmacogenomics project. This proposal contains of an extensive training plan for Eleazar Eskin including courses at UCSD in order for him to obtain the necessary background in hypertension and genetics for the project. Daniel O'Connor and Nicholas Schork will mentor Eleazar throughout the project. This training and mentoring will put Eleazar in a position to have his research have a larger impact in medicine. This research is relevant to public health because it attempts to understand the relation between an individual's genetic variation and disease outcomes. Identification of the variants that are implicated in complex disease is the first step in the ultimate goal of tailoring treatments to an individual's genetics.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25HL080079-06
Application #
7845473
Study Section
Special Emphasis Panel (ZHL1-CSR-M (O1))
Program Officer
Commarato, Michael
Project Start
2006-06-01
Project End
2012-05-31
Budget Start
2010-06-01
Budget End
2012-05-31
Support Year
6
Fiscal Year
2010
Total Cost
$137,098
Indirect Cost
Name
University of California Los Angeles
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Olde Loohuis, Loes M; Mangul, Serghei; Ori, Anil P S et al. (2018) Transcriptome analysis in whole blood reveals increased microbial diversity in schizophrenia. Transl Psychiatry 8:96
Mangul, Serghei; Yang, Harry Taegyun; Strauli, Nicolas et al. (2018) ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues. Genome Biol 19:36
Rahmani, Elior; Schweiger, Regev; Shenhav, Liat et al. (2018) BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference. Genome Biol 19:141
Kang, Eun Yong; Lee, Cue Hyunkyu; Furlotte, Nicholas A et al. (2018) An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures. Genetics 209:685-698
Hormozdiari, Farhad; Zhu, Anthony; Kichaev, Gleb et al. (2017) Widespread Allelic Heterogeneity in Complex Traits. Am J Hum Genet 100:789-802
Bilow, Michael; Crespo, Fernando; Pan, Zhicheng et al. (2017) Simultaneous Modeling of Disease Status and Clinical Phenotypes To Increase Power in Genome-Wide Association Studies. Genetics 205:1041-1047
Joo, Jong Wha J; Kang, Eun Yong; Org, Elin et al. (2016) Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure. Genetics 204:1379-1390
Kang, Eun Yong; Park, Yurang; Li, Xiao et al. (2016) ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis. G3 (Bethesda) 6:1793-8
Duong, Dat; Zou, Jennifer; Hormozdiari, Farhad et al. (2016) Using genomic annotations increases statistical power to detect eGenes. Bioinformatics 32:i156-i163
Kang, Eun Yong; Martin, Lisa J; Mangul, Serghei et al. (2016) Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data. Genetics 204:1057-1064

Showing the most recent 10 out of 97 publications