A Unified Framework Integrating Parent-of-Origin Effects for Association Study

Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting is related to several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we generalize the natural and orthogonal interactions (NOIA) framework to allow for estimation of both main allelic effects and POEs. We develop a statistical (Stat-POE) model that has the orthogonal estimates of parameters including the POEs. We conducted simulation studies for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat- POE and Func-POE models under HWE for quantitative traits

Genetic Association Analysis of Complex Diseases Incorporating Intermediate Phenotype Information

Genetic researchers often collect disease related quantitative traits in addition to disease status because they are interested in understanding the pathophysiology of disease processes. In genome-wide association (GWA) studies, these quantitative phenotypes may be relevant to disease development and serve as intermediate phenotypes or they could be behavioral or other risk factors that predict disease risk. Statistical tests combining both disease status and quantitative risk factors should be more powerful than case-control studies, as the former incorporates more information about the disease. In this paper, we proposed a modified inverse-variance weighted meta-analysis method to combine disease status and quantitative intermediate phenotype information. The simulation results showed that when an intermediate phenotype was available, the inverse-variance weighted method had more power than did a case-control study of complex diseases, especially in identifying susceptibility loci having minor effects. We further applied this modified meta- analysis to a study of imputed lung cancer genotypes with smoking data in 1154 cases and 1137 matched controls. The most significant SNPs came from the CHRNA3-CHRNA5-CHRNB4 region on chromosome 15q24–25.1, which has been replicated in many other studies. Our results confirm that this CHRNA region is associated with both lung cancer development and smoking behavior. We also detected three significant SNPs—rs1800469, rs1982072, and rs2241714—in the promoter region of the TGFB1 gene on chromosome 19 (p = 1.46 X 10-5,1.18 X 10-5, and 6.57 X 10-6, respectively). The SNP rs1800469 is reported to be associated with chronic obstructive pulmonary disease and lung cancer in cigarette smokers. The present study is the first GWA study to replicate this result. Signals in the 3q26 region were also identified in the meta-analysis. We demonstrate the intermediate phenotype can potentially enhance the power of complex disease association analysis and the modified meta-analysis method is robust to incorporate intermediate phenotype or other quantitative risk factor in the analysis

Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction

Background: Over the past decades, approaches for diagnosing and treating cancer have seen significant improvement. However, the variability of patient and tumor characteristics has limited progress on methods for prognosis prediction. The development of high-throughput omics technologies now provides multiple approaches for characterizing tumors. Although a large number of published studies have focused on integration of multi-omics data and use of pathway-level models for cancer prognosis prediction, there still exists a gap of knowledge regarding the prognostic landscape across multi-omics data for multiple cancer types using both gene-level and pathway-level predictors. Methods: In this study, we systematically evaluated three often available types of omics data (gene expression, copy number variation and somatic point mutation) covering both DNA-level and RNA-level features. We evaluated the landscape of predictive performance of these three omics modalities for 33 cancer types in the TCGA using a Lasso or Group Lasso-penalized Cox model and either gene or pathway level predictors. Results: We constructed the prognostic landscape using three types of omics data for 33 cancer types on both the gene and pathway levels. Based on this landscape, we found that predictive performance is cancer type dependent and we also highlighted the cancer types and omics modalities that support the most accurate prognostic models. In general, models estimated on gene expression data provide the best predictive performance on either gene or pathway level and adding copy number variation or somatic point mutation data to gene expression data does not improve predictive performance, with some exceptional cohorts including low grade glioma and thyroid cancer. In general, pathway-level models have better interpretative performance, higher stability and smaller model size across multiple cancer types and omics data types relative to gene-level models. Conclusions: Based on this landscape and comprehensively comparison, models estimated on gene expression data provide the best predictive performance on either gene or pathway level. Pathway-level models have better interpretative performance, higher stability and smaller model size relative to gene-level models

Seeking gene relationships in gene expression data using support vector machine regression

Several genetic determinants responsible for individual variation in gene expression have been located using linkage and association analyses. These analyses have revealed regulatory relationships between genes. The heritability of expression variation as a quantitative phenotype reflects its underlying genetic architecture. Using support vector machine regression (SVMR) and gene ontological information, we proposed an approach to identify gene relationships in expression data provided by Genetic Analysis Workshop 15 that would facilitate subsequent genetic analyses. A group of related genes were selected for a shared biological theme, and SVMR was trained to form a regression model using the training gene expressions. The model was subsequently used to search for and capture similarly related genes. SVMR shows promising capability in modeling and seeking gene relationships through expression data

Relative effects of mutability and selection on single nucleotide polymorphisms in transcribed regions of the human genome

<p>Abstract</p> <p>Motivation</p> <p>Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation in humans. However, the factors that affect SNP density are poorly understood. The goal of this study was to estimate the relative effects of mutability and selection on SNP density in transcribed regions of human genes. It is important for prediction of the regions that harbor functional polymorphisms.</p> <p>Results</p> <p>We used frequency-validated SNPs resulting from single-nucleotide substitutions. SNPs were subdivided into five functional categories: (i) 5' untranslated region (UTR) SNPs, (ii) 3' UTR SNPs, (iii) synonymous SNPs, (iv) SNPs producing conservative missense mutations, and (v) SNPs producing radical missense mutations. Each of these categories was further subdivided into nine mutational categories on the basis of the single-nucleotide substitution type. Thus, 45 functional/mutational categories were analyzed. The relative mutation rate in each mutational category was estimated on the basis of published data. The proportion of segregating sites (PSSs) for each functional/mutational category was estimated by dividing the observed number of SNPs by the number of potential sites in the genome for a given functional/mutational category. By analyzing each functional group separately, we found significant positive correlations between PSSs and relative mutation rates (Spearman's correlation coefficient, at least r = 0.96, df = 9, <it>P </it>< 0.001). We adjusted the PSSs for the mutation rate and found that the functional category had a significant effect on SNP density (F = 5.9, df = 4, <it>P </it>= 0.001), suggesting that selection affects SNP density in transcribed regions of the genome. We used analyses of variance and covariance to estimate the relative effects of selection (functional category) and mutability (relative mutation rate) on the PSSs and found that approximately 87% of variation in PSS was due to variation in the mutation rate and approximately 13% was due to selection, suggesting that the probability that a site located in a transcribed region of a gene is polymorphic mostly depends on the mutability of the site.</p

Genome-wide algorithm for detecting CNV associations with diseases

<p>Abstract</p> <p>Background</p> <p>SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP data using the marker intensities. However, these algorithms lack specificity to detect small CNVs owing to the high false positive rate when calling CNVs based on the intensity values. Therefore, the resulting association tests lack power even if the CNVs affecting disease risk are common. An alternative procedure called PennCNV uses information from both the marker intensities as well as the genotypes and therefore has increased sensitivity.</p> <p>Results</p> <p>By using the hidden Markov model (HMM) implemented in PennCNV to derive the probabilities of different copy number states which we subsequently used in a logistic regression model, we developed a new genome-wide algorithm to detect CNV associations with diseases. We compared this new method with association test applied to the most probable copy number state for each individual that is provided by PennCNV after it performs an initial HMM analysis followed by application of the Viterbi algorithm, which removes information about copy number probabilities. In one of our simulation studies, we showed that for large CNVs (number of SNPs ≥ 10), the association tests based on PennCNV calls gave more significant results, but the new algorithm retained high power. For small CNVs (number of SNPs <it><</it>10), the logistic algorithm provided smaller average p-values (e.g., <it>p </it>= 7.54<it>e </it>- 17 when relative risk <it>RR </it>= 3.0) in all the scenarios and could capture signals that PennCNV did not (e.g., <it>p </it>= 0.020 when <it>RR </it>= 3.0). From a second set of simulations, we showed that the new algorithm is more powerful in detecting disease associations with small CNVs (number of SNPs ranging from 3 to 5) under different penetrance models (e.g., when <it>RR </it>= 3.0, for relatively weak signals, <it>power </it>= 0.8030 comparing to 0.2879 obtained from the association tests based on PennCNV calls). The new method was implemented in software GWCNV. It is freely available at <url>http://gwcnv.sourceforge.net</url>, distributed under a GPL license.</p> <p>Conclusions</p> <p>We conclude that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than the existing HMM algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.</p

Forward-Time Simulations of Human Populations with Complex Diseases

Due to the increasing power of personal computers, as well as the availability of flexible forward-time simulation programs like simuPOP, it is now possible to simulate the evolution of complex human diseases using a forward-time approach. This approach is potentially more powerful than the coalescent approach since it allows simulations of more than one disease susceptibility locus using almost arbitrary genetic and demographic models. However, the application of such simulations has been deterred by the lack of a suitable simulation framework. For example, it is not clear when and how to introduce disease mutants—especially those under purifying selection—to an evolving population, and how to control the disease allele frequencies at the last generation. In this paper, we introduce a forward-time simulation framework that allows us to generate large multi-generation populations with complex diseases caused by unlinked disease susceptibility loci, according to specified demographic and evolutionary properties. Unrelated individuals, small or large pedigrees can be drawn from the resulting population and provide samples for a wide range of study designs and ascertainment methods. We demonstrate our simulation framework using three examples that map genes associated with affection status, a quantitative trait, and the age of onset of a hypothetical cancer, respectively. Nonadditive fitness models, population structure, and gene–gene interactions are simulated. Case-control, sibpair, and large pedigree samples are drawn from the simulated populations and are examined by a variety of gene-mapping methods

Dynamic Positional Trees for Structural Image Analysis

Abstract Dynamic positional trees are a significant extension of dynamic trees, incorporating movable nodes. This addition makes sequence tracking viable within the model, but requires a new formulation to incorporate the prior over positions. The model is implemented using a structured variational procedure, and is illustrated on synthetic raytraced images and image sequences. We consider the problem of structural image analysis and in particular the inference of scene properties from image data. We are especially concerned with image decomposition, that is obtaining the characteristic parts of an image and the relationships between them. The components of an image are not independent of each other; certain objects are expected to occur together, and objects are made up of different subcomponents. One way of thinking of this problem is by analogy with parsing a language; we are interested in parsing images. However, the important characteristics and structure in an image is significantly different from linguistic data. Those familiar with work on dynamic trees will be aware that they have been developed in the context of single static images [15, 1, 13]. It would be desirable if the benefits of the dynamic tree approach could also be made available for image sequences. Introducing a sequence model into the basic dynamic tree formalism is not straightforward as a change in the position of an object is reflected in a change in the connectivity structure of the dynamic tree. This change would be hard to predict from the previous time slice and would be an inelegant representation of the dynamics: the connectivity structure is supposed to represent the structural characteristics of an object, most of which will be preserved during movement. Here the dynamic tree is modified to incorporate position variables, resulting in a model where object movement can be represented in terms of a change in position components of the nodes representing that object