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Statistical Applications in Genetics and Molecular Biology

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https://www.readbyqxmd.com/read/30507552/a-practical-approach-to-adjusting-for-population-stratification-in-genome-wide-association-studies-principal-components-and-propensity-scores-pcaps
#1
Huaqing Zhao, Nandita Mitra, Peter A Kanetsky, Katherine L Nathanson, Timothy R Rebbeck
Genome-wide association studies (GWAS) are susceptible to bias due to population stratification (PS). The most widely used method to correct bias due to PS is principal components (PCs) analysis (PCA), but there is no objective method to guide which PCs to include as covariates. Often, the ten PCs with the highest eigenvalues are included to adjust for PS. This selection is arbitrary, and patterns of local linkage disequilibrium may affect PCA corrections. To address these limitations, we estimate genomic propensity scores based on all statistically significant PCs selected by the Tracy-Widom (TW) statistic...
December 4, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/30447151/a-novel-method-to-accurately-calculate-statistical-significance-of-local-similarity-analysis-for-high-throughput-time-series
#2
Fang Zhang, Ang Shan, Yihui Luan
In recent years, a large number of time series microbial community data has been produced in molecular biological studies, especially in metagenomics. Among the statistical methods for time series, local similarity analysis is used in a wide range of environments to capture potential local and time-shifted associations that cannot be distinguished by traditional correlation analysis. Initially, the permutation test is popularly applied to obtain the statistical significance of local similarity analysis. More recently, a theoretical method has also been developed to achieve this aim...
November 17, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/30231014/assessing-genome-wide-significance-for-the-detection-of-differentially-methylated-regions
#3
Christian M Page, Linda Vos, Trine B Rounge, Hanne F Harbo, Bettina K Andreassen
DNA methylation plays an important role in human health and disease, and methods for the identification of differently methylated regions are of increasing interest. There is currently a lack of statistical methods which properly address multiple testing, i.e. control genome-wide significance for differentially methylated regions. We introduce a scan statistic (DMRScan), which overcomes these limitations. We benchmark DMRScan against two well established methods (bumphunter, DMRcate), using a simulation study based on real methylation data...
September 19, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/30205662/a-variable-selection-approach-in-the-multivariate-linear-model-an-application-to-lc-ms-metabolomics-data
#4
Marie Perrot-Dockès, Céline Lévy-Leduc, Julien Chiquet, Laure Sansonnet, Margaux Brégère, Marie-Pierre Étienne, Stéphane Robin, Grégory Genta-Jouve
Omic data are characterized by the presence of strong dependence structures that result either from data acquisition or from some underlying biological processes. Applying statistical procedures that do not adjust the variable selection step to the dependence pattern may result in a loss of power and the selection of spurious variables. The goal of this paper is to propose a variable selection procedure within the multivariate linear model framework that accounts for the dependence between the multiple responses...
September 8, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/30169328/biology-challenging-statistics
#5
EDITORIAL
Michael P H Stumpf
No abstract text is available yet for this article.
August 30, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/30142087/editorial-change-at-statistical-applications-in-genetics-and-molecular-biology
#6
Torsten Krüger
No abstract text is available yet for this article.
August 24, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/30059350/a-test-for-detecting-differential-indirect-trans-effects-between-two-groups-of-samples
#7
Nimisha Chaturvedi, Renée X de Menezes, Jelle J Goeman, Wessel van Wieringen
Integrative analysis of copy number and gene expression data can help in understanding the cis and trans effect of copy number aberrations on transcription levels of genes involved in a pathway. To analyse how these copy number mediated gene-gene interactions differ between groups of samples we propose a new method, named dNET. Our method uses ridge regression to model the network topology involving one gene's expression level, its gene dosage and the expression levels of other genes in the network. The interaction parameters are estimated by fitting the model per gene for all samples together...
July 31, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/30007059/on-the-relation-between-the-true-and-sample-correlations-under-bayesian-modelling-of-gene-expression-datasets
#8
Royi Jacobovic
No abstract text is available yet for this article.
July 14, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29975669/empirical-bayesian-approach-to-testing-multiple-hypotheses-with-separate-priors-for-left-and-right-alternatives
#9
Naveen K Bansal, Mehdi Maadooliat, Steven J Schrodi
No abstract text is available yet for this article.
July 5, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29959888/comparisons-of-classification-methods-for-viral-genomes-and-protein-families-using-alignment-free-vectorization
#10
COMPARATIVE STUDY
Hsin-Hsiung Huang, Shuai Hao, Saul Alarcon, Jie Yang
No abstract text is available yet for this article.
June 30, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29897889/a-statistical-method-for-measuring-activation-of-gene-regulatory-networks
#11
Gustavo H Esteves, Luiz F L Reis
MOTIVATION: Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. RESULTS: We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation...
June 13, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29897888/multi-locus-data-distinguishes-between-population-growth-and-multiple-merger-coalescents
#12
Jere Koskela
We introduce a low dimensional function of the site frequency spectrum that is tailor-made for distinguishing coalescent models with multiple mergers from Kingman coalescent models with population growth, and use this function to construct a hypothesis test between these model classes. The null and alternative sampling distributions of the statistic are intractable, but its low dimensionality renders them amenable to Monte Carlo estimation. We construct kernel density estimates of the sampling distributions based on simulated data, and show that the resulting hypothesis test dramatically improves on the statistical power of a current state-of-the-art method...
June 13, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29886455/non-parametric-estimation-of-population-size-changes-from-the-site-frequency-spectrum
#13
Berit Lindum Waltoft, Asger Hobolth
Changes in population size is a useful quantity for understanding the evolutionary history of a species. Genetic variation within a species can be summarized by the site frequency spectrum (SFS). For a sample of size n, the SFS is a vector of length n - 1 where entry i is the number of sites where the mutant base appears i times and the ancestral base appears n - i times. We present a new method, CubSFS, for estimating the changes in population size of a panmictic population from an observed SFS. First, we provide a straightforward proof for the expression of the expected site frequency spectrum depending only on the population size...
June 11, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29874197/bayesian-inference-of-selection-in-the-wright-fisher-diffusion-model
#14
Jeffrey J Gory, Radu Herbei, Laura S Kubatko
The increasing availability of population-level allele frequency data across one or more related populations necessitates the development of methods that can efficiently estimate population genetics parameters, such as the strength of selection acting on the population(s), from such data. Existing methods for this problem in the setting of the Wright-Fisher diffusion model are primarily likelihood-based, and rely on numerical approximation for likelihood computation and on bootstrapping for assessment of variability in the resulting estimates, requiring extensive computation...
June 6, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29708886/noise-robust-assessment-of-snp-array-based-cnv-calls-through-local-noise-estimation-of-log-r-ratios
#15
Nele Cosemans, Peter Claes, Nathalie Brison, Joris Robert Vermeesch, Hilde Peeters
Arrays based on single nucleotide polymorphisms (SNPs) have been successful for the large scale discovery of copy number variants (CNVs). However, current CNV calling algorithms still have limitations in detecting CNVs with high specificity and sensitivity, especially in case of small (<100 kb) CNVs. Therefore, this study presents a simple statistical analysis to evaluate CNV calls from SNP arrays in order to improve the noise-robustness of existing CNV calling algorithms. The proposed approach estimates local noise of log R ratios and returns the probability that a certain observation is different from this log R ratio noise level...
April 28, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29601301/on-a-mutual-information-estimator-with-exponentially-decaying-bias-by-zhang-and-zheng
#16
COMMENT
Jialin Zhang, Chen Chen
Zhang, Z. and Zheng, L. (2015): "A mutual information estimator with exponentially decaying bias," Stat. Appl. Genet. Mol. Biol., 14, 243-252, proposed a nonparametric estimator of mutual information developed in entropic perspective, and demonstrated that it has much smaller bias than the plugin estimator yet with the same asymptotic normality under certain conditions. However it is incorrectly suggested in their article that the asymptotic normality could be used for testing independence between two random elements on a joint alphabet...
March 30, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29453930/ensemble-survival-tree-models-to-reveal-pairwise-interactions-of-variables-with-time-to-events-outcomes-in-low-dimensional-setting
#17
Jean-Eudes Dazard, Hemant Ishwaran, Rajeev Mehlotra, Aaron Weinberg, Peter Zimmerman
Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance...
February 17, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29420308/additive-varying-coefficient-model-for-nonlinear-gene-environment-interactions
#18
Cen Wu, Ping-Shou Zhong, Yuehua Cui
Gene-environment (G×E) interaction plays a pivotal role in understanding the genetic basis of complex disease. When environmental factors are measured continuously, one can assess the genetic sensitivity over different environmental conditions on a disease trait. Motivated by the increasing awareness of gene set based association analysis over single variant based approaches, we proposed an additive varying-coefficient model to jointly model variants in a genetic system. The model allows us to examine how variants in a gene set are moderated by an environment factor to affect a disease phenotype...
February 8, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29397393/distance-correlation-based-gene-set-analysis-in-longitudinal-studies
#19
Jiehuan Sun, Jose D Herazo-Maya, Xiu Huang, Naftali Kaminski, Hongyu Zhao
Longitudinal gene expression profiles of subjects are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene sets that have coordinated changes with relevant clinical outcomes over time from these data could provide significant insights into the molecular basis of disease progression and lead to better treatments. In this article, we propose a Distance-Correlation based Gene Set Analysis (dcGSA) method for longitudinal gene expression data...
February 5, 2018: Statistical Applications in Genetics and Molecular Biology
https://www.readbyqxmd.com/read/29381476/tests-for-comparison-of-multiple-endpoints-with-application-to-omics-data
#20
Marco Marozzi
In biomedical research, multiple endpoints are commonly analyzed in "omics" fields like genomics, proteomics and metabolomics. Traditional methods designed for low-dimensional data either perform poorly or are not applicable when analyzing high-dimensional data whose dimension is generally similar to, or even much larger than, the number of subjects. The complex biochemical interplay between hundreds (or thousands) of endpoints is reflected by complex dependence relations. The aim of the paper is to propose tests that are very suitable for analyzing omics data because they do not require the normality assumption, are powerful also for small sample sizes, in the presence of complex dependence relations among endpoints, and when the number of endpoints is much larger than the number of subjects...
January 30, 2018: Statistical Applications in Genetics and Molecular Biology
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