Medical Genetics Commons^™

Epigenome-Wide Association Study Of Kidney Function Identifies Trans-Ethnic And Ethnic-Specific Loci, Charles E. Breeze, Anna Batorsky, Mi Kyeong Lee, Mindy D. Szeto, Xiaoguang Xu, Daniel L. Mccartney, Rong Jiang, Amit Patki, Holly J. Kramer, James M. Eales, Laura Raffield, Leslie Lange, Ethan Lange, Peter Durda, Yongmei Liu, Russ P. Tracy, David Van Den Berg, Nhlbi Trans-Omics For Precision Medicine (Topmed) Consortium, Topmed Mesa Multi-Omics Working Group, Kathryn L. Evans, William E. Kraus, Donna K. Arnett

Epidemiology and Environmental Health Faculty Publications

BACKGROUND: DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach.

METHODS: The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear …

Subject Level Clustering Using A Negative Binomial Model For Small Transcriptomic Studies., Qian Li, Janelle R. Noel-Macdonnell, Devin C. Koestler, Ellen L. Goode, Brooke L. Fridley

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput 'omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. N ≥ 500. A common issue when analyzing limited samples of RNA-seq count data is that the data follows an over-dispersed distribution, and thus a Negative Binomial likelihood model is often used. Thus, we have developed a Negative Binomial model-based (NBMB) clustering approach for application to RNA-seq studies.

RESULTS: We have developed a Negative …

User-Centered Design Of Multi-Gene Sequencing Panel Reports For Clinicians., Elizabeth Cutting, Meghan Banchero, Amber L. Beitelshees, James J. Cimino, Guilherme Del Fiol, Ayse P. Gurses, Mark A. Hoffman, Linda Jo Bone Jeng, Kensaku Kawamoto, Mark Kelemen, Harold Alan Pincus, Alan R. Shuldiner, Marc S. Williams, Toni I. Pollin, Casey Lynnette Overby

Manuscripts, Articles, Book Chapters and Other Papers

The objective of this study was to develop a high-fidelity prototype for delivering multi-gene sequencing panel (GS) reports to clinicians that simulates the user experience of a final application. The delivery and use of GS reports can occur within complex and high-paced healthcare environments. We employ a user-centered software design approach in a focus group setting in order to facilitate gathering rich contextual information from a diverse group of stakeholders potentially impacted by the delivery of GS reports relevant to two precision medicine programs at the University of Maryland Medical Center. Responses from focus group sessions were transcribed, coded and …

Functional Linear Models Extensions Uncover Pleiotropic Effects Of Chronic Pain Phenotypes, Dmitri V. Zaykin, L. Qing, G. D. Slade, R. Dubner, R. B. Fillingim, J. D. Greenspan, R. Ohrbach, W. Maixner, L. B. Diatchenko, Olga A. Vsevolozhskaya

Biostatistics Presentations

Growing scientific evidence suggests that intricate interactions of genetic risk factors with environmental exposures play a major role in the development of chronic pain conditions. In studies of relative contribution of an individual’s genetic composition to the perception of pain, the general characteristics of pain sensitivity are typically measured by a wide range of different, yet possibly etiologically related pain phenotypes. Testing each of these pain-perception traits individually is subject to problems of multiple testing and low statistical power. Furthermore, pain-related traits may share common etiology and comprise binary, categorical, and quantitative measurements. In the current study, we propose a …

Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi

School of Computing: Faculty Publications

This paper considers two types of protein data. First, data about protein function described in a number of ways, such as, GO terms and PFAM families. Second, data about whether individual proteins are experimentally associated with cancer by an anomalous elevation or lowering of their expressions within cancerous cells. We combine these two types of protein data and test whether the first type of data, that is, the functional descriptors, can predict the second type of data, that is, cancer-relatedness. By using data mining and machine learning, we derive a classifier algorithm that using only GO term and PFAM family …

Genetic Studies Of Complex Human Diseases: Characterizing Snp-Disease Associations Using Bayesian Networks, Bing Han, Xue-Wen Chen, Zohreh Talebizadeh, Hua Xu

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis, and treatment of complex human diseases. Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype.

Results

To address the problems of computational methods in epistatic interaction detection, we propose a score-based Bayesian network structure learning method, EpiBN, to detect epistatic interactions. We apply the proposed method to both simulated datasets and …

Down-Weighting Overlapping Genes Improves Gene Set Analysis, Adi Tarca, Sorin Draghici, Gaurav Bhatti, Roberto Romero

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set.

Results

In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the …

Structural Properties Of Thermoresponsive Poly(N-Isopropylacrylamide)-Poly(Ethyleneglycol) Microgels, J. Clara-Rahola, A. Fernandez-Nieves, B. Sierra-Martin, A. B. South, L. Andrew Lyon, J. Kohlbrecher, A. F. Barbero

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The application of RNA interference to treat disease is an important yet challenging concept in modern medicine. In particular, small interfering RNA (siRNA) have shown tremendous promise in the treatment of cancer. However, siRNA show poor pharmacological properties, which presents a major hurdle for effective disease treatment especially through intravenous delivery routes. In response to these shortcomings, a variety of nanoparticle carriers have emerged, which are designed to encapsulate, protect, and transport siRNA into diseased cells. To be effective as carrier vehicles, nanoparticles must overcome a series of biological hurdles throughout the course of delivery. As a result, one promising …

A Supermatrix Analysis Of Genomic, Morphological, And Paleontological Data From Crown Cetacea, Jonathan H. Geisler, Michael R. Mcgowen, Guang Yang, John Gatesy

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Cetacea (dolphins, porpoises, and whales) is a clade of aquatic species that includes the most massive, deepest diving, and largest brained mammals. Understanding the temporal pattern of diversification in the group as well as the evolution of cetacean anatomy and behavior requires a robust and well-resolved phylogenetic hypothesis. Although a large body of molecular data has accumulated over the past 20 years, DNA sequences of cetaceans have not been directly integrated with the rich, cetacean fossil record to reconcile discrepancies among molecular and morphological characters.

Results

We combined new nuclear DNA sequences, including segments of six genes (~2800 …

Bio::Phylo-Phyloinformatic Analysis Using Perl, Rutger A. Vos, Jason Caravas, Klaas Hartmann, Mark A. Jensen, Chase Miller

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Phyloinformatic analyses involve large amounts of data and metadata of complex structure. Collecting, processing, analyzing, visualizing and summarizing these data and metadata should be done in steps that can be automated and reproduced. This requires flexible, modular toolkits that can represent, manipulate and persist phylogenetic data and metadata as objects with programmable interfaces.

Results

This paper presents Bio::Phylo, a Perl5 toolkit for phyloinformatic analysis. It implements classes and methods that are compatible with the well-known BioPerl toolkit, but is independent from it (making it easy to install) and features a richer API and a data model that is …

Droid: The Drosophila Interactions Database, A Comprehensive Resource For Annotated Gene And Protein Interactions, Jingkai Yu, Svetlana Pacifico, Guozhen Liu, Russell L. Finley Jr

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, Drosophila melanogaster. …

A Database And Tool, Im Browser, For Exploring And Integrating Emerging Gene And Protein Interaction Data For Drosophila, Svetlana Pacifico, Guozhen Liu, Stephen Guest, Jodi R. Parrish, Farshad Fotouhi, Russell L. Finley Jr

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins …

K-Spmm: A Database Of Murine Spermatogenic Promoters Modules & Motifs, Yi Lu, Adrian E. Platts, G Charles Ostermeier, Stephen A. Krawetz

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Understanding the regulatory processes that coordinate the cascade of gene expression leading to male gamete development has proven challenging. Research has been hindered in part by an incomplete picture of the regulatory elements that are both characteristic of and distinctive to the broad population of spermatogenically expressed genes.

Description

K-SPMM, a database of murine Spermatogenic Promoters Modules and Motifs, has been developed as a web-based resource for the comparative analysis of promoter regions and their constituent elements in developing male germ cells. The system contains data on 7,551 genes and 11,715 putative promoter regions …

Incremental Genetic K-Means Algorithm And Its Application In Gene Expression Data Analysis, Yi Lu, Shiyong Lu, Farshad Fotouhi, Youping Deng, Susan J. Brown

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.

Results

In this paper, we propose a new clustering …