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Activity Number: 180 - Statistical Methods for Functional Genomic and Epigenomic Data
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #305260
Title: Identifying Patterns of Multi-Genetic/Epigenetic Factors via Non-Parametric Clustering
Author(s): Meredith Ray* and Lauren Sobral and S. Hasan Arshad and John Holloway and Wilfried JJ Karmaus and Hongmei Zhang
Companies: University of Memphis and University of Memphis and University of Southampton and University of Southampton and University of Memphis and University of Memphis
Keywords: Non-parametric clustering; K-means; genetics; epigenetics; data mining

Due to the rapid growth in technology, various types of high-dimensional and high throughput data have been quickly generated in the area of genetic and epigenetic studies. There is a great need to develop mining methods with information from various aspects incorporated. We propose a novel clustering approach built upon genetic and epigenetic data including single nucleotide polymorphisms (SNPs), DNA methylation, and gene expression for a set of genes. The method is developed under the K-means framework. We formulate a novel Euclidean-distance-based metric to assess distances between clustering objects, and this metric takes into account complex joint effects of SNPs and DNA methylation on the expression of a gene. Simulations were conducted and demonstrated high sensitivity, specificity, and accuracy with respect to cluster assignment. We apply the method to a data set from a birth cohort on the Isle of Wight, UK, which includes SNPs, DNA methylation, and gene expressions, to identify clusters of children across eczema-related genes and examine genetic and epigenetic patterns of each cluster.

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

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