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Full-Text Articles in Physical Sciences and Mathematics
Function-On-Scalar Regression For Genetic Association Studies, Olga A. Vsevolozhskaya, Dmitri V. Zaykin, Qing Lu
Function-On-Scalar Regression For Genetic Association Studies, Olga A. Vsevolozhskaya, Dmitri V. Zaykin, Qing Lu
Olga A. Vsevolozhskaya
We propose a general framework to perform gene/region based analysis of sequencing data by regressing a functional response on one or multiple scalar predictors. Next generation sequencing technologies make it possible to uncover genetic information from millions of variants. Since the observed sequenced variants are very close in their genetic positions, we can consider them to be realizations of random continuous functions. Therefore, instead of analyzing multiple individual genetic variants per subject, we can estimate the underlying continuous function and treat it as a functional response in a regression model. Smoothing splines are used to fit these functional responses by …
Functional Analysis Of Variance For Association Studies, Olga A. Vsevolozhskaya, Dmitri V. Zaykin, Mark C. Greenwood, Changshuai Wei, Qing Lu
Functional Analysis Of Variance For Association Studies, Olga A. Vsevolozhskaya, Dmitri V. Zaykin, Mark C. Greenwood, Changshuai Wei, Qing Lu
Olga A. Vsevolozhskaya
While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. …
Penalized Functional Regression For Next-Generation Sequencing Studies, Olga A. Vsevolozhskaya
Penalized Functional Regression For Next-Generation Sequencing Studies, Olga A. Vsevolozhskaya
Olga A. Vsevolozhskaya
Advances in next-generation sequencing (NGS) technologies make it possible to examining nearly every variant on the human genome. Because of the high density of variants, genotypes within a genetic region can be thought of as a finite sample realization of some underlying stochastic process. The aim of vast majority of NSG studies is to explore an association between a set of genetic variants and qualitative/quantitative traits. To address this aim, we propose a general framework based on functional dependent variable and univariate covariates. We fit our model using penalized least squares criterion. We note that within this framework, the spline …
Pairwise Comparison Of Treatment Levels In Functional Analysis Of Variance With Application To Erythrocyte Hemolysis, Olga A. Vsevolozhskaya, Mark C. Greenwood, Dmitri Holodov
Pairwise Comparison Of Treatment Levels In Functional Analysis Of Variance With Application To Erythrocyte Hemolysis, Olga A. Vsevolozhskaya, Mark C. Greenwood, Dmitri Holodov
Olga A. Vsevolozhskaya
Motivated by a practical need for the comparison of hemolysis curves at various treatment levels, we propose a novel method for pairwise comparison of mean functional responses. The hemolysis curves—the percent hemolysis as a function of time—of mice erythrocytes (red blood cells) by hydrochloric acid have been measured among different treatment levels. This data set fits well within the functional data analysis paradigm, in which a time series is considered as a realization of the underlying stochastic process or a smooth curve. Previous research has only provided methods for identifying some differences in mean curves at different times. We propose …