244,520 research outputs found
A fast algorithm for detecting gene-gene interactions in genome-wide association studies
With the recent advent of high-throughput genotyping techniques, genetic data
for genome-wide association studies (GWAS) have become increasingly available,
which entails the development of efficient and effective statistical
approaches. Although many such approaches have been developed and used to
identify single-nucleotide polymorphisms (SNPs) that are associated with
complex traits or diseases, few are able to detect gene-gene interactions among
different SNPs. Genetic interactions, also known as epistasis, have been
recognized to play a pivotal role in contributing to the genetic variation of
phenotypic traits. However, because of an extremely large number of SNP-SNP
combinations in GWAS, the model dimensionality can quickly become so
overwhelming that no prevailing variable selection methods are capable of
handling this problem. In this paper, we present a statistical framework for
characterizing main genetic effects and epistatic interactions in a GWAS study.
Specifically, we first propose a two-stage sure independence screening (TS-SIS)
procedure and generate a pool of candidate SNPs and interactions, which serve
as predictors to explain and predict the phenotypes of a complex trait. We also
propose a rates adjusted thresholding estimation (RATE) approach to determine
the size of the reduced model selected by an independence screening.
Regularization regression methods, such as LASSO or SCAD, are then applied to
further identify important genetic effects. Simulation studies show that the
TS-SIS procedure is computationally efficient and has an outstanding finite
sample performance in selecting potential SNPs as well as gene-gene
interactions. We apply the proposed framework to analyze an
ultrahigh-dimensional GWAS data set from the Framingham Heart Study, and select
23 active SNPs and 24 active epistatic interactions for the body mass index
variation. It shows the capability of our procedure to resolve the complexity
of genetic control.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS771 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Detection of Epigenomic Network Community Oncomarkers
In this paper we propose network methodology to infer prognostic cancer
biomarkers based on the epigenetic pattern DNA methylation. Epigenetic
processes such as DNA methylation reflect environmental risk factors, and are
increasingly recognised for their fundamental role in diseases such as cancer.
DNA methylation is a gene-regulatory pattern, and hence provides a means by
which to assess genomic regulatory interactions. Network models are a natural
way to represent and analyse groups of such interactions. The utility of
network models also increases as the quantity of data and number of variables
increase, making them increasingly relevant to large-scale genomic studies. We
propose methodology to infer prognostic genomic networks from a DNA
methylation-based measure of genomic interaction and association. We then show
how to identify prognostic biomarkers from such networks, which we term
`network community oncomarkers'. We illustrate the power of our proposed
methodology in the context of a large publicly available breast cancer dataset
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