Association-based Methods Research Articles

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications.

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.

Context similarity based hybrid approach for extracting hindi multiword expressions

In natural language processing, multiword expressions (MWEs) play a significant role in understanding the context and meaning of a sentence. A MWE comprises two or more words that are handled as if they were one. MWE has the property that the constituent words are consistent and are often used in related contexts. Hindi is used as a case study. We employed three properties: linguistic or syntactical pattern, a relationship between constituent words, and context similarity and proposed a three-phase hybrid approach to extract MWE from unstructured Hindi text. Experimental analysis and comparison of results on the TDIL dataset show the superiority of the proposed hybrid method over the context-based method and association-based methods.

Genetic Epidemiology of Complex Phenotypes.

Genetic and environmental factors are critical elements in most common complex disease. Genetics is increasingly being recognized to play a substantive role in the susceptibility, prognosis, and treatment of common diseases. Due to recent and rapid advancements in characterization of genetic variants and large-scale genotyping platforms, multiple genes and genetic variants have now been identified for common, complex diseases. The most efficient method for gene identification at present appears to be large-scale association-based studies, which integrate genetic and epidemiological principles. As the strategy for gene identification studies has shifted toward genetic association-based methods rather than traditional linkage analysis, epidemiological methods are increasingly being integrated into genetic investigations and in public health research. Consequently, the disciplines of genetics and epidemiology, which historically have functioned separately, have been integrated into a hybrid discipline referred to as genetic epidemiology. In this chapter, we review methods for establishing the genetic burden of complex genetic disease, followed by methods for gene and/or genetic variant identification. When appropriate, we will highlight the epidemiological issues and clinical applications that guide these methods.

Correlation and association analyses in microbiome study integrating multiomics in health and disease.

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Augmented state GM-PHD filter with registration errors for multi-target tracking by Doppler radars

For multi-sensor multi-target tracking, traditional association-based methods treat data association and registration separately. However, they actually affect each other. The probability hypothesis density (PHD) filter has the distinct advantage that it avoids the complicated data association. In this paper, we propose an augmented state Gaussian mixture PHD (GM-PHD) filter with registration errors for multi-target tracking by Doppler radars. First, we construct the linear Gaussian dynamics and measurement model of the augmented state, which is comprised of target states and sensor biases. Then, related equations are derived when the standard GM-PHD filter is applied to the augmented state system. To effectively utilize Doppler measurements in the augmented state GM-PHD, the sequential processing method is adopted, i.e., updating target states and sensor biases with polar measurements first; and then updating sequentially target states with Doppler measurements; finally, computing weights with both polar and Doppler measurements. Simulation results show that the proposed filter is effective, and it has more robust performance in dense clutter.

An Association-Based Unified Framework for Mining Features and Opinion Words

Mining features and opinion words is essential for fine-grained opinion analysis of customer reviews. It is observed that semantic dependencies naturally exist between features and opinion words, even among features or opinion words themselves. In this article, we employ a corpus statistics association measure to quantify the pairwise word dependencies and propose a generalized association-based unified framework to identify features, including explicit and implicit features, and opinion words from reviews. We first extract explicit features and opinion words via an association-based bootstrapping method (ABOOT). ABOOT starts with a small list of annotated feature seeds and then iteratively recognizes a large number of domain-specific features and opinion words by discovering the corpus statistics association between each pair of words on a given review domain. Two instances of this ABOOT method are evaluated based on two particular association models, likelihood ratio tests (LRTs) and latent semantic analysis (LSA). Next, we introduce a natural extension to identify implicit features by employing the recognized known semantic correlations between features and opinion words. Experimental results illustrate the benefits of the proposed association-based methods for identifying features and opinion words versus benchmark methods.

Cloud computing-based TagSNP selection algorithm for human genome data.

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.

Clinical genetic research 2: Genetic epidemiology of complex phenotypes.

Genetic factors play a substantive role in the susceptibility to common diseases. Due to recent and rapid advancements in characterization of genetic variants and large-scale genotyping platforms, multiple genes and genetic variants have now been identified for common, complex diseases. The most efficient method for gene identification at present appears to be large-scale association-based studies, which integrate genetic and epidemiological principles. As the strategy for gene identification studies has shifted towards genetic association-based methods rather than traditional linkage analysis, epidemiological methods are increasingly being integrated into genetic investigations. Consequently, the disciplines of genetics and epidemiology, which historically have functioned separately, have been integrated into a discipline referred to as genetic epidemiology. In this chapter, we review methods for establishing the genetic burden of complex genetic disease, followed by methods for gene and/or genetic variant identification and when appropriate we highlight the epidemiological issues that guide these methods.

A ratiometric-based measure of gene co-expression.

BackgroundGene co-expression analysis has previously been based on measures that include correlation coefficients and mutual information, as well as newcomers such as MIC. These measures depend primarily on the degree of association between the RNA levels of two genes and to a lesser extent on their variability. They focus on the similarity of expression value trajectories that change in like manner across samples. However there are relationships of biological interest for which these classical measures are expected to be insensitive. These include genes whose expression levels are ratiometrically stable and genes whose variance is tightly constrained. Large-scale studies of relatively homogeneous samples, including single cell RNA-seq, are experimental settings in which such relationships might be especially pertinent.ResultsWe develop and implement a ratiometric approach for detecting gene associations (abbreviated RA). It is based on the coefficient of variation of the measured expression ratio of each pair of genes. We apply it to a collection of lymphoblastoid RNA-seq data from the 1000 Genomes Project Consortium, a typical sample set with high overall homogeneity. RA is a selective method, reporting in this case ~1/4 of all possible gene pairs, yet these relationships include a distilled picture of biological relationships previously found by other methods. In addition, RA reveals expression relationships that are not detected by traditional correlation and mutual information methods. We also analyze data from individual lymphoblastoid cells and show that desirable properties of the RA method extend to single-cell RNA-seq.ConclusionWe show that our ratiometric method identifies biologically significant relationships that are often missed or low-ranked by conventional association-based methods when applied to a relatively homogenous dataset. The results open new questions about the regulatory mechanisms that produce strong RA relationships. RA is scalable and potentially well suited for the analysis of thousands of bulk-RNA or single-cell transcriptomes.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-331) contains supplementary material, which is available to authorized users.

Identifying causal rare variants of disease through family-based analysis of Genetics Analysis Workshop 17 data set

Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes (KDR, VEGFA, VEGFC, and FLT1). Our results suggest that both linkage and association tests with families show promise for identifying rare variants.

Cell Breathing Techniques for Load Balancing in Wireless LANs

Maximizing network throughput while providing fairness is one of the key challenges in wireless LANs (WLANs). This goal is typically achieved when the load of access points (APs) is balanced. Recent studies on operational WLANs, however, have shown that AP load is often substantially uneven. To alleviate such imbalance of load, several load balancing schemes have been proposed. These schemes commonly require proprietary software or hardware at the user side for controlling the user-AP association. In this paper we present a new load balancing technique by controlling the size of WLAN cells (i.e., AP's coverage range), which is conceptually similar to cell breathing in cellular networks. The proposed scheme does not require any modification to the users neither the IEEE 802.11 standard. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. We develop a set of polynomial time algorithms that find the optimal beacon power settings which minimize the load of the most congested AP. We also consider the problem of network-wide min-max load balancing. Simulation results show that the performance of the proposed method is comparable with or superior to the best existing association-based methods.

X-APL: An Improved Family-Based Test of Association in the Presence of Linkage for the X Chromosome

Family-based association methods have been developed primarily for autosomal markers. The X-linked sibling transmission/disequilibrium test (XS-TDT) and the reconstruction-combined TDT for X-chromosome markers (XRC-TDT) are the first association-based methods for testing markers on the X chromosome in family data sets. These are valid tests of association in family triads or discordant sib pairs but are not theoretically valid in multiplex families when linkage is present. Recently, XPDT and XMCPDT, modified versions of the pedigree disequilibrium test (PDT), were proposed. Like the PDT, XPDT compares genotype transmissions from parents to affected offspring or genotypes of discordant siblings; however, the XPDT can have low power if there are many missing parental genotypes. XMCPDT uses a Monte Carlo sampling approach to infer missing parental genotypes on the basis of true or estimated population allele frequencies. Although the XMCPDT was shown to be more powerful than the XPDT, variability in the statistic due to the use of an estimate of allele frequency is not properly accounted for. Here, we present a novel family-based test of association, X-APL, a modification of the test for association in the presence of linkage (APL) test. Like the APL, X-APL can use singleton or multiplex families and properly infers missing parental genotypes in linkage regions by considering identity-by-descent parameters for affected siblings. Sampling variability of parameter estimates is accounted for through a bootstrap procedure. X-APL can test individual marker loci or X-chromosome haplotypes. To allow for different penetrances in males and females, separate sex-specific tests are provided. Using simulated data, we demonstrated validity and showed that the X-APL is more powerful than alternative tests. To show its utility and to discuss interpretation in real-data analysis, we also applied the X-APL to candidate-gene data in a sample of families with Parkinson disease.

Association mapping: methodologies, strategies, and issues

Recent advances in molecular genetic technology allow for detailed characterization of genetic variation and easy cost-efficient accumulation of such data, even for large human samples. One such advance that presents incredible opportunities for identifying associations between genetic polymorphisms and disease-related phenotypes is the ability to quickly type a large number of single-nucleotide polymorphisms (SNPs). Contributors to Group 10 of Genetic Analysis Workshop 14 explored the potential of SNP genotypes for the association mapping of disease-related genes in family-based studies. Using both real data involving alcoholism susceptibility, made available by the Collaborative Study on the Genetics of Alcoholism (COGA), and simulated data involving personality-disorder susceptibility, group members investigated specific methodological issues involved in association mapping, such as multiple testing, single SNPs vs. combinations and haplotypes, and the effect of linkage disequilibrium on SNP-based linkage; evaluated existing methodologies for association mapping using SNPs, short-tandem repeats (STRs), or a combination of the two; and introduced new or modified association-mapping methods, including a gamma random effects (GRE) model and the quantitative trait linkage disequilibrium (QTLD) test. These papers are unified by the application of association-based methods to analyze SNPs, microsatellite markers, or both, to identify chromosomal regions harboring genes that contribute to quantitative endophenotype variation, and thus to disease risk. Their diversity attests to the breadth and flexibility of association-mapping approaches to the genetics of complex disease.

Understanding GPS. Principles and applications

For multi-sensor multi-target tracking, traditional association-based methods treat data association and registration separately. However, they actually affect each other. The probability hypothesis density (PHD) filter has the distinct advantage that it avoids the complicated data association. In this paper, we propose an augmented state Gaussian mixture PHD (GM-PHD) filter with registration errors for multi-target tracking by Doppler radars. First, we construct the linear Gaussian dynamics and measurement model of the augmented state, which is comprised of target states and sensor biases. Then, related equations are derived when the standard GM-PHD filter is applied to the augmented state system. To effectively utilize Doppler measurements in the augmented state GM-PHD, the sequential processing method is adopted, i.e., updating target states and sensor biases with polar measurements first; and then updating sequentially target states with Doppler measurements; finally, computing weights with both polar and Doppler measurements. Simulation results show that the proposed filter is effective, and it has more robust performance in dense clutter.