Genetics and Genomics Commons^™

Unified Methods For Feature Selection In Large-Scale Genomic Studies With Censored Survival Outcomes, Lauren Spirko-Burns, Karthik Devarajan

COBRA Preprint Series

One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease's process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous data sets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards …

Supervised Dimension Reduction For Large-Scale "Omics" Data With Censored Survival Outcomes Under Possible Non-Proportional Hazards, Lauren Spirko-Burns, Karthik Devarajan

COBRA Preprint Series

The past two decades have witnessed significant advances in high-throughput ``omics" technologies such as genomics, proteomics, metabolomics, transcriptomics and radiomics. These technologies have enabled simultaneous measurement of the expression levels of tens of thousands of features from individual patient samples and have generated enormous amounts of data that require analysis and interpretation. One specific area of interest has been in studying the relationship between these features and patient outcomes, such as overall and recurrence-free survival, with the goal of developing a predictive ``omics" profile. Large-scale studies often suffer from the presence of a large fraction of censored observations and potential …

Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang

COBRA Preprint Series

Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …

Computational Model For Survey And Trend Analysis Of Patients With Endometriosis : A Decision Aid Tool For Ebm, Salvo Reina, Vito Reina, Franco Ameglio, Mauro Costa, Alessandro Fasciani

COBRA Preprint Series

Endometriosis is increasingly collecting worldwide attention due to its medical complexity and social impact. The European community has identified this as a “social disease”. A large amount of information comes from scientists, yet several aspects of this pathology and staging criteria need to be clearly defined on a suitable number of individuals. In fact, available studies on endometriosis are not easily comparable due to a lack of standardized criteria to collect patients’ informations and scarce definitions of symptoms. Currently, only retrospective surgical stadiation is used to measure pathology intensity, while the Evidence Based Medicine (EBM) requires shareable methods and correct …

Why Odds Ratio Estimates Of Gwas Are Almost Always Close To 1.0, Yutaka Yasui

COBRA Preprint Series

“Missing heritability” in genome-wide association studies (GWAS) refers to the seeming inability for GWAS data to capture the great majority of genetic causes of a disease in comparison to the known degree of heritability for the disease, in spite of GWAS’ genome-wide measures of genetic variations. This paper presents a simple mathematical explanation for this phenomenon, assuming that the heritability information exists in GWAS data. Specifically, it focuses on the fact that the great majority of association measures (in the form of odds ratios) from GWAS are consistently close to the value that indicates no association, explains why this occurs, …

Modeling Protein Expression And Protein Signaling Pathways, Donatello Telesca, Peter Muller, Steven Kornblau, Marc Suchard, Yuan Ji

COBRA Preprint Series

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The …

A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi

COBRA Preprint Series

Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two matrices, W and H, each with nonnegative entries, V ~ WH. NMF has been shown to have a unique parts-based, sparse representation of the data. The nonnegativity constraints in NMF allow only additive combinations of the data which enables it to learn parts that have distinct physical representations in reality. In the last few years, NMF has been successfully applied in a variety of areas such as natural language processing, information retrieval, image processing, speech recognition …

A Bayesian Model Averaging Approach For Observational Gene Expression Studies, Xi Kathy Zhou, Fei Liu, Andrew J. Dannenberg

COBRA Preprint Series

Identifying differentially expressed (DE) genes associated with a sample characteristic is the primary objective of many microarray studies. As more and more studies are carried out with observational rather than well controlled experimental samples, it becomes important to evaluate and properly control the impact of sample heterogeneity on DE gene finding. Typical methods for identifying DE genes require ranking all the genes according to a pre-selected statistic based on a single model for two or more group comparisons, with or without adjustment for other covariates. Such single model approaches unavoidably result in model misspecification, which can lead to increased error …

Minimum Description Length Measures Of Evidence For Enrichment, Zhenyu Yang, David R. Bickel

COBRA Preprint Series

In order to functionally interpret differentially expressed genes or other discovered features, researchers seek to detect enrichment in the form of overrepresentation of discovered features associated with a biological process. Most enrichment methods treat the p-value as the measure of evidence using a statistical test such as the binomial test, Fisher's exact test or the hypergeometric test. However, the p-value is not interpretable as a measure of evidence apart from adjustments in light of the sample size. As a measure of evidence supporting one hypothesis over the other, the Bayes factor (BF) overcomes this drawback of the p-value but lacks …

A Bayesian Shared Component Model For Genetic Association Studies, Juan J. Abellan, Carlos Abellan, Juan R. Gonzalez

COBRA Preprint Series

We present a novel approach to address genome association studies between single nucleotide polymorphisms (SNPs) and disease. We propose a Bayesian shared component model to tease out the genotype information that is common to cases and controls from the one that is specific to cases only. This allows to detect the SNPs that show the strongest association with the disease. The model can be applied to case-control studies with more than one disease. In fact, we illustrate the use of this model with a dataset of 23,418 SNPs from a case-control study by The Welcome Trust Case Control Consortium (2007) …

Minimum Description Length And Empirical Bayes Methods Of Identifying Snps Associated With Disease, Ye Yang, David R. Bickel

COBRA Preprint Series

The goal of determining which of hundreds of thousands of SNPs are associated with disease poses one of the most challenging multiple testing problems. Using the empirical Bayes approach, the local false discovery rate (LFDR) estimated using popular semiparametric models has enjoyed success in simultaneous inference. However, the estimated LFDR can be biased because the semiparametric approach tends to overestimate the proportion of the non-associated single nucleotide polymorphisms (SNPs). One of the negative consequences is that, like conventional p-values, such LFDR estimates cannot quantify the amount of information in the data that favors the null hypothesis of no disease-association.

We …

The Strength Of Statistical Evidence For Composite Hypotheses: Inference To The Best Explanation, David R. Bickel

COBRA Preprint Series

A general function to quantify the weight of evidence in a sample of data for one hypothesis over another is derived from the law of likelihood and from a statistical formalization of inference to the best explanation. For a fixed parameter of interest, the resulting weight of evidence that favors one composite hypothesis over another is the likelihood ratio using the parameter value consistent with each hypothesis that maximizes the likelihood function over the parameter of interest. Since the weight of evidence is generally only known up to a nuisance parameter, it is approximated by replacing the likelihood function with …

Targeted Genomic Signature Profiling With Quasi-Alignment Statistics, Rao Mallik Kotamarti, Douglas W. Raiford, Michael Hahsler, Yuhang Wang, Monnie Mcgee, Maggie Dunham

COBRA Preprint Series

Genome databases continue to expand with no change in the basic format of sequence data. The prevalent use of the Classic alignment based search tools like BLAST have significantly pushed the limits of Genome Isolate research. The relatively new frontier of Metagenomic research deals with thousands of diverse genomes with newer demands beyond the current homologue search and analysis. Compressing sequence data into a complex form could facilitate a broader range of sequence analyses. To this end, this research explores reorganizing sequence data as complex Markov signatures also known as Extensible Markov Models. Markov models have found successful application in …

A Novel Topology For Representing Protein Folds, Mark R. Segal

COBRA Preprint Series

Various topologies for representing three dimensional protein structures have been advanced for purposes ranging from prediction of folding rates to ab initio structure prediction. Examples include relative contact order, Delaunay tessellations, and backbone torsion angle distributions. Here we introduce a new topology based on a novel means for operationalizing three dimensional proximities with respect to the underlying chain. The measure involves first interpreting a rank-based representation of the nearest neighbors of each residue as a permutation, then determining how perturbed this permutation is relative to an unfolded chain. We show that the resultant topology provides improved association with folding and …

Fitting Ace Structural Equation Models To Case-Control Family Data, Kristin N. Javaras, James I. Hudson, Nan M. Laird

COBRA Preprint Series

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). To this end, we describe a ACE model for binary family data and then introduce an approach to fitting the model to case-control family data. The structural equation model, which has been described previously, combines a general-family extension of …

Detection Of Recurrent Copy Number Alterations In The Genome: A Probabilistic Approach, Oscar M. Rueda, Ramon Diaz-Uriarte

COBRA Preprint Series

Copy number variation (CNV) in genomic DNA is linked to a variety of human diseases (including cancer, HIV acquisition, autoimmune and neurodegenerative diseases), and array-based CGH (aCGH) is currently the main technology to locate CNVs. Several methods can analyze aCGH data at the single sample level, but disease-critical genes are more likely to be found in regions that are common or recurrent among samples. Unfortunately, defining recurrent CNV regions remains a challenge. Moreover, the heterogeneous nature of many diseases requires that we search for CNVs that affect only some subsets of the samples (without prior knowledge of which regions and …

Finding Recurrent Regions Of Copy Number Variation: A Review, Oscar M. Rueda, Ramon Diaz-Uriarte

COBRA Preprint Series

Copy number variation (CNV) in genomic DNA is linked to a variety of human diseases, and array-based CGH (aCGH) is currently the main technology to locate CNVs. Although many methods have been developed to analyze aCGH from a single array/subject, disease-critical genes are more likely to be found in regions that are common or recurrent among subjects. Unfortunately, finding recurrent CNV regions remains a challenge. We review existing methods for the identification of recurrent CNV regions. The working definition of ``common'' or ``recurrent'' region differs between methods, leading to approaches that use different types of input (discretized output from a …

The Strength Of Statistical Evidence For Composite Hypotheses With An Application To Multiple Comparisons, David R. Bickel

COBRA Preprint Series

The strength of the statistical evidence in a sample of data that favors one composite hypothesis over another may be quantified by the likelihood ratio using the parameter value consistent with each hypothesis that maximizes the likelihood function. Unlike the p-value and the Bayes factor, this measure of evidence is coherent in the sense that it cannot support a hypothesis over any hypothesis that it entails. Further, when comparing the hypothesis that the parameter lies outside a non-trivial interval to the hypotheses that it lies within the interval, the proposed measure of evidence almost always asymptotically favors the correct hypothesis …

Improving Gsea For Analysis Of Biologic Pathways For Differential Gene Expression Across A Binary Phenotype , Irina Dinu, John D. Potter, Thomas Mueller, Qi Liu, Adeniyi J. Adewale, Gian S. Jhangri, Gunilla Einecke, Konrad S. Famulski, Philip Halloran, Yutaka Yasui

COBRA Preprint Series

Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of single genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the single-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS). Specifically, we illustrate, in a simulation study, that GSEA gives statistical significance to gene sets that have no gene associated …

Exploration Of Distributional Models For A Novel Intensity-Dependent Normalization , Nicola Lama, Patrizia Boracchi, Elia Mario Biganzoli

COBRA Preprint Series

Currently used gene intensity-dependent normalization methods, based on regression smoothing techniques, usually approach the two problems of location bias detrending and data re-scaling without taking into account the censoring characteristic of certain gene expressions produced by experiment measurement constraints or by previous normalization steps. Moreover, the bias vs variance balance control of normalization procedures is not often discussed but left to the user's experience. Here an approximate maximum likelihood procedure to fit a model smoothing the dependences of log-fold gene expression differences on average gene intensities is presented. Central tendency and scaling factor were modeled by means of B-splines smoothing …

A Unifying Approach For Haplotype Analysis Of Quantitative Traits In Family-Based Association Studies: Testing And Estimating Gene-Environment Interactions With Complex Exposure Variables, Stijn Vansteelandt, Christoph Lange

COBRA Preprint Series

We propose robust and e±cient tests and estimators for gene-environment/gene-drug interactions in family-based association studies. The methodology is designed for studies in which haplotypes, quantitative pheno- types and complex exposure/treatment variables are analyzed. Using causal inference methodology, we derive family-based association tests and estimators for the genetic main effects and the interactions. The tests and estimators are robust against population admixture and strati¯cation without requiring adjustment for confounding variables. We illustrate the practical relevance of our approach by an application to a COPD study. The data analysis suggests a gene-environment interaction between a SNP in the Serpine gene and smok- …

New Statistical Paradigms Leading To Web-Based Tools For Clinical/Translational Science, Knut M. Wittkowski

COBRA Preprint Series

As the field of functional genetics and genomics is beginning to mature, we become confronted with new challenges. The constant drop in price for sequencing and gene expression profiling as well as the increasing number of genetic and genomic variables that can be measured makes it feasible to address more complex questions. The success with rare diseases caused by single loci or genes has provided us with a proof-of-concept that new therapies can be developed based on functional genomics and genetics.

Common diseases, however, typically involve genetic epistasis, genomic pathways, and proteomic pattern. Moreover, to better understand the underlying biologi-cal …