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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 …
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 …