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Activity Number: 501
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #320904 View Presentation
Title: Detecting EQTLs: A Fast Analysis Protocol Using High-Dimensional Sequencing Data
Author(s): Kai Kammers* and Ingo Ruczinski and Margaret A. Taub and Joshua Martin and Lisa R. Yanek and Lewis Becker and Rasika A. Mathias and Jeffrey Leek
Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and The GeneSTAR Program and The GeneSTAR Program and The GeneSTAR Program and The GeneSTAR Program and Johns Hopkins Bloomberg School of Public Health
Keywords: eQTLs ; RNA-seq ; Batch Effects ; Megakaryocytes ; Stem Cells ; gEUVADIS
Abstract:

The goal of an eQTL analysis is to detect patterns of transcript expression related to specific genetic variants. In this talk we present our recently developed analysis protocol for performing extensive eQTL analyses - from raw RNA-seq reads and genotype data to eQTL plots showing gene-SNP interactions. We explain in detail how expression and genotype data are filtered, transformed, and batch corrected. We also discuss possible pitfalls and artifacts that may occur when analyzing genomic data from different sources jointly. Our protocol is tested on a publicly available data set of the RNA-seq project from the gEUVADIS consortium and also applied to recently generated omics data from the GeneSTAR project at Johns Hopkins. One goal of this project is to understand the biology of platelet aggregation. Therefore, we examined genetic and transcriptomic data from megakaryocytes (MKs), the precursor cells for anucleate platelets, that are derived from induced pluripotent stem cells (iPSCs). Given a high genetic and transcriptomic integrity of MKs, we found several hundred cis-eQTLs in European Americans and African Americans and see a high replication between the two groups.


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