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Activity Number: 545
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #320969 View Presentation
Title: Analysis of Ultra-High-Dimensional Polycystic Ovary Syndrome Genome Using DC-RR
Author(s): Jill Lundell* and Guifang Fu
Companies: Utah State University and Utah State University
Keywords: Genetics ; GWAS ; Logistic Regression
Abstract:

Analysis of genome-wide data is riddled with unprecedented challenges because of the newest second generation sequencing techniques. A novel statistical approach, DC-RR, has recently been developed to inspect the genome-wide associations after overcoming high dimensionality and the complex correlation structure present in the whole genome data. The DC-RR approach selects the most promising SNPs by removing noise using a Distance Correlation based feature screening approach. The correlation structure is then aggressively addressed using the ridge penalized multiple logistic regression model. The DC-RR method has demonstrated a gain in power and reduced false discovery rate relative to other popular methods. In this article, we implemented DC-RR approach to detect susceptibility gene for Polycystic ovary (PCOS) disease. After analyzing the dataset that has 1042 polycystic ovary patients and 3055 controls over more than 700,000 SNPs, a few significant SNPs were located. These findings will better assist researchers in public health for diagnosis and treatment of the PCOS disease.


Authors who are presenting talks have a * after their name.

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