1,721,015 research outputs found
Statistical aspects of haplotype-based association studies
Get PDFA decade ago, genomewide association studies were proposed as a tool to unravel the genetic basis of complex diseases. It is only now that they are becoming practical realities due to improved technology and reduced genotyping costs. For such studies, the issues of power and efficiency are crucial due to the quantity of markers genotyped and the moderate effect sizes involved. Haplotype-based analysis incorporates information from multiple markers, and so is potentially more powerful than single-SNP analysis. Unfortunately, not only is it computationally more intensive, but since haplotypes are not directly observed, there exists a major analytical challenge with haplotype association analysis. Several methods are available to infer individual haplotypes from unphased genotype data, but using the inferred haplotypes in the ensuing association analysis can result in biased estimates and reduced power. We investigate the situations for which the disadvantages of the imputation process may outweigh its convenience. In addition, we describe alternatives to imputation which result in efficient haplotype association analysis. For case-control studies, we develop methods for use in genomewide studies which account for the correlation between SNPs in multiple test correction. Simulation studies based on the HapMap data showed that the proposed method performs well in realistic situations. We applied it to a case-control dataset of 2,300 SNPs to test for association with rheumatoid arthritis. For quantitative trait loci, we focus on gains in power which may be made via selective genotyping designs, where only those individuals with extreme phenotypes are genotyped. Because selection depends on the phenotype, the resulting data cannot be properly analyzed by standard statistical methods. We provide appropriate likelihoods for assessing the effects of genotypes and haplotypes on quantitative traits under such designs. We demonstrate that the likelihood-based methods are highly effective in identifying causal variants, and are substantially more powerful than existing methods. We initially consider two practical designs, then extend the methods to a two-phase sampling design. Additionally, we provide methods to test for haplotype-disease association in the presence of covariates. Simulations demonstrate the effectiveness of these likelihood-based methods
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Statistical analysis of haplotypes, untyped SNPs, and CNVs in genome-wide association studies
Get PDFMissing data arise in genetic association studies when one is interested in assessing the effects of haplotypes, untyped single nucleotide polymorphisms (SNPs) or copy number variants (CNVs). Haplotypes are combinations of nucleotides at multiple loci along individual homologous chromosomes, and the use of haplotypes tends to yield more efficient analysis of disease association than SNPs. Untyped SNPs are SNPs that are not on the genotyping chips used in the study (i.e., missing on all study subjects), and the analysis of untyped SNPs can facilitate localization of disease-causing variants and permit meta-analysis of association studies with different genotyping platforms. A CNV refers to the duplication or deletion of a segment of DNA sequence compared to a reference genome assembly, and can play a causal role in genetic diseases. In the first part of the proposal, we provide a general likelihood-based framework for making inference on the effects of haplotypes or untyped SNPs and their interactions with environmental variables. Unlike most of the existing methods, we allow genetic and environmental variables to be correlated. We show that the maximum likelihood estimators are consistent, asymptotically normal, and asymptotically efficient and we develop EM algorithms to implement the corresponding inference procedures. We conduct extensive simulation studies and apply the methods to a genome-wide association study (GWAS) of lung cancer. In the second part, we focus on comparing two approaches in the analysis of untyped SNPs. The maximum likelihood approach integrates prediction of untyped genotypes and estimation of association parameters into a single framework and yields consistent and efficient estimators of genetic effects and gene-environment interactions with proper variance estimators. The imputation approach is a two-stage strategy which first imputes the untyped genotypes by either the most likely genotypes or the expected genotype counts and then uses the imputed values in downstream association analysis. We conduct extensive simulation studies to compare the bias, type I error, power, and confidence interval coverage between the two methods under various situations. In addition, we provide an illustration with genome-wide data from the Wellcome Trust Case-Control Consortium (WTCCC). In the third part, we present a general framework for the integrated analysis of CNVs and SNPs in association studies, including the analysis of total copy number as a special case. We use allele-specific copy numbers (ASCNs) to describe both the copy number and allelic variations of a locus. %The joint effects of CNVs and SNPs on the disease are formulated in terms of allele-specific copy numbers (ASCNs). Our approach combines the ASCN calling and association analysis into a single step while allowing for differential errors. We construct likelihood functions that properly account for the case-control sampling and measurement errors. We establish the asymptotic properties of the maximum likelihood estimators and develop EM algorithms to implement the proposed inference procedures. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to a GWAS of schizophrenia
Variable Selection, Sparse Meta-Analysis and Genetic Risk Prediction for Genome-Wide Association Studies
Get PDFGenome-wide association studies (GWAS) usually involve more than half a million single nucleotide polymorphisms (SNPs). The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Recently developed variable selection methods allow the joint analysis for GWAS data, but they tend to miss causal SNPs that are marginally uncorrelated with disease and have high false discovery rates (FDRs). Genetic risk prediction becomes highly challenging when the number of causal variants is large and many of the effects are weak. Existing methods mostly rely on marginal regression estimates, and their prediction power is quite limited. In meta-analysis, the involvement of multiple studies adds one more layer of complexity to variable selection. While existing variable selection methods can be potentially applied to meta-analysis, they require direct access to raw data, which are often difficult to be obtained. In the first part of this dissertation, we introduce GWASelect, a statistically powerful and computationally efficient variable selection method for analyzing GWAS data. This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus capable of capturing causal SNPs that are marginally correlated with disease as well as those that are marginally uncorrelated with disease. A special resampling mechanism is built into the method to reduce false-positive findings. Simulation studies demonstrate that the GWASelect performs well under a wide spectrum of linkage disequilibrium patterns and can be substantially more powerful than existing methods in capturing causal variants while having a lower FDR. In addition, the regression models based on the GWASelect tend to yield more accurate prediction of disease risk than existing methods. In the second part, we propose a new approach, Sparse Meta-Analysis (SMA), which performs variable selection for meta-analysis based solely on summary statistics and allows the effect sizes of each covariate to vary among studies. We show that the SMA enjoys the oracle property if the estimated covariance matrix of the parameter estimators from each study is available. We also consider the situations in which the summary statistics include only the variances or no variance/covariance information at all. Simulation studies and real data analysis demonstrate that the proposed methods perform well. Since summary statistics are far more accessible than raw data, our methods have broader applications in high-dimensional meta-analysis than existing ones. In the third part, we investigate the issue of genetic risk prediction when the number of true causal SNPs is large and many of the effect sizes are small. We show that the estimators obtained from marginal logistic regression can be severely biased and that using these estimators for prediction can lead to highly inaccurate results. To construct a joint-effects model, we propose a new method based on the smoothly clipped absolute deviation-supporting vector machine (SCAD-SVM). We conduct a series of simulation studies to show that our method outperforms the methods based on marginal estimators. We further assess the performance of our method by applying it to real GWAS studies.Doctor of Philosoph
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Model checking and prediction with censored data
Get PDFThe class of semiparametric transformation models provides a very general framework for studying the effects of (possibly time-dependent) covariates on survival time and recurrent event times. Although many theoretical and methodological advances have been made for transformation models, the methods for assessing the adequacy of these models have not been formally studied. In the first part of this dissertation, we introduce appropriate residuals for these models and consider the cumulative sums of the residuals. Under the assumed model, the cumulative-sum processes converge weakly to zero-mean Gaussian processes whose distributions can be approximated through Monte Carlo simulation. These results enable one to assess, both visually and numerically, how unusual the observed residual patterns are in reference to their null distributions. The residual patterns can also be used to determine the nature of model misspecification. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. A colon cancer study is provided for illustration. Attributable fractions are commonly used to measure the impact of risk factors on disease incidence in the population. These static measures can be extended to functions of time when the time to disease occurrence or event time is of interest. In the second part of this dissertation, we deal with nonparametric and semiparametric estimation of attributable fraction functions for cohort studies with potentially censored event time data.The semiparametric models include the familiar proportional hazards model and a broad class of transformation models. The proposed estimators are shown to be consistent, asymptotically normal and asymptotically efficient. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. A cardiovascular health study is provided. There is a tremendous current interest in using multiple clinical and/or genetic factors to predict progression of disease. To determine which set of factors is most predictive, the predictive accuracy of multiple factors must be quantified. The existing measures are focused on the proportion of variation explained by the factors. These measures are not easily interpreted and have rarely been used in clinical practice. In the third part of this dissertation, we develop measures of predictive accuracy based on the survival curves associated with different sets of predictors. Such measures extend positive and negative predictive values to time-to-event outcomes and multiple factors and have direct clinical relevance. We develop estimators for these measures under flexible censoring mechanisms. The proposed estimators are shown to be consistent and asymptotically normal. Simple Monte Carlo methods are developed to approximate the asymptotic distributions. Simulation studies show that the proposed methods perform well in practical situations. The Mayo primary biliary cirrhosis (PBC) study is provided for illustration
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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