Analyzing Genome-wide Association Study Data with the R Package genMOSS

REMI: Regression with marginal information and its application in genome-wide association studies

In this study, we consider the problem of variable selection and estimation in high-dimensional linear regression models when the complete data are not accessible, but only certain marginal information or summary statistics are available. This problem is motivated from the Genome-wide association studies (GWAS) that have been widely used to identify risk variants underlying complex human traits/diseases. With a large number of completed GWAS, statistical methods using summary statistics become more and more important because of restricted accessibility to individual-level data sets. Theoretica…

Analyzing genome-wide association studies with an FDR controlling modification of the Bayesian information criterion

The prevailing method of analyzing GWAS data is still to test each marker individually, although from a statistical point of view it is quite obvious that in case of complex traits such single marker tests are not ideal. Recently several model selection approaches for GWAS have been suggested, most of them based on LASSO-type procedures. Here we will discuss an alternative model selection approach which is based on a modification of the Bayesian Information Criterion (mBIC2) which was previously shown to have certain asymptotic optimality properties in terms of minimizing the misclassification…

wtest: an R Package for Testing Main and Interaction Effect in Genotype Data with Binary Traits

This R package evaluates main and pair-wise interaction effect of single nucleotide polymorphisms (SNPs) via the W-test, scalable to whole genome-wide data sets. The package provides fast and accurate p-value estimation of genetic markers, as well as diagnostic checking on the probability distributions. It allows flexible stage-wise or exhaustive association testing in a user-friendly interface. Availability: The package is available in CRAN, or from website: http://www2.ccrb.cuhk.edu.hk/wtest

Higher Criticism Tuned Regression For Weak And Sparse Signals

Here we propose a novel searching scheme for a tuning parameter in high-dimensional penalized regression methods to address variable selection and modeling when sample sizes are limited compared to the data dimensions. Our method is motivated by high-throughput biological data such as genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS). We propose a new estimate of the regularization parameter $λ$ in penalized regression methods based on an estimated lower bound of the proportion of false null hypotheses with confidence $(1 - α)$. The bound is estimated by appl…

Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data

Genome annotation is an important issue in biology which has long been addressed with gene prediction methods and manual experiments requiring biological expertise. The expanding Next Generation Sequencing technologies and their enhanced precision allow a new approach to the domain: the segmentation of RNA-Seq data to determine gene boundaries. Because of its almost linear complexity, we propose to use the Pruned Dynamic Programming Algorithm, which performances had been acknowledged for CGH arrays, for Seq-experiment outputs. This requires the adaptation of the algorithm to the negative binom…

Poly-Omic Prediction of Complex Traits: OmicKriging

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic or other omics-level data. We translate the omic similarity int…

varbvs: Fast Variable Selection for Large-scale Regression

We introduce varbvs, a suite of functions written in R and MATLAB for regression analysis of large-scale data sets using Bayesian variable selection methods. We have developed numerical optimization algorithms based on variational approximation methods that make it feasible to apply Bayesian variable selection to very large data sets. With a focus on examples from genome-wide association studies, we demonstrate that varbvs scales well to data sets with hundreds of thousands of variables and thousands of samples, and has features that facilitate rapid data analyses. Moreover, varbvs allows for …

带状相似矩阵的邻接约束层次聚类及其在基因组学中的应用

Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics

针对基因组数据分析中邻接约束层次聚类的二次复杂度问题，提出基于远距对象相似性可忽略假设的准线性复杂度实现，验证了其生物学有效性与高效性，提供R包adjclust。

Distributions and Statistical Power of Optimal Signal-Detection Methods In Finite Cases

In big data analysis for detecting rare and weak signals among $n$ features, some grouping-test methods such as Higher Criticism test (HC), Berk-Jones test (B-J), and $ϕ$-divergence test share the similar asymptotical optimality when $n \rightarrow \infty$. However, in practical data analysis $n$ is frequently small and moderately large at most. In order to properly apply these optimal tests and wisely choose them for practical studies, it is important to know how to get the p-values and statistical power of them. To address this problem in an even broader context, this paper provides analytic…

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported. We describe the implementation, provide examples, validate the package with a reference implementation, and compare runtime and memory usage with other implementations. The new software proves to scale best with the number of features, samples, trees, and features tried for splitting. Finally, we show that ranger is the fastest and most memory efficient implementation of random forests…

kalis：用于R中局部血统推断的Li & Stephens模型的现代实现

kalis: A Modern Implementation of the Li & Stephens Model for Local Ancestry Inference in R

针对现有Li & Stephens模型后验解码无法扩展至大规模基因组数据的问题，本研究在R语言中开发了高性能软件包kalis，利用多核并行与CPU向量指令集实现大规模局部血统推断。