| 669c6a90 |
using SNP arrays.},
journal = {Biostatistics},
year = {2011},
volume = {12},
pages = {33--50},
number = {1},
month = {Jan},
abstract = {Submicroscopic changes in chromosomal DNA copy number dosage are common
and have been implicated in many heritable diseases and cancers.
Recent high-throughput technologies have a resolution that permits
the detection of segmental changes in DNA copy number that span thousands
of base pairs in the genome. Genomewide association studies (GWAS)
may simultaneously screen for copy number phenotype and single nucleotide
polymorphism (SNP) phenotype associations as part of the analytic
strategy. However, genomewide array analyses are particularly susceptible
to batch effects as the logistics of preparing DNA and processing
thousands of arrays often involves multiple laboratories and technicians,
or changes over calendar time to the reagents and laboratory equipment.
Failure to adjust for batch effects can lead to incorrect inference
and requires inefficient post hoc quality control procedures to exclude
regions that are associated with batch. Our work extends previous
model-based approaches for copy number estimation by explicitly modeling
batch and using shrinkage to improve locus-specific estimates of
copy number uncertainty. Key features of this approach include the
use of biallelic genotype calls from experimental data to estimate
batch-specific and locus-specific parameters of background and signal
without the requirement of training data. We illustrate these ideas
using a study of bipolar disease and a study of chromosome 21 trisomy.
The former has batch effects that dominate much of the observed variation
in the quantile-normalized intensities, while the latter illustrates
the robustness of our approach to a data set in which approximately
27\% of the samples have altered copy number. Locus-specific estimates
of copy number can be plotted on the copy number scale to investigate
mosaicism and guide the choice of appropriate downstream approaches
for smoothing the copy number as a function of physical position.
The software is open source and implemented in the R package crlmm
at Bioconductor (http:www.bioconductor.org).},
doi = {10.1093/biostatistics/kxq043},
institution = {Department of Oncology, Johns Hopkins University School of Medicine,
Baltimore, MD 21205, USA. rscharpf@jhsph.edu},
language = {eng},
medline-pst = {ppublish},
owner = {rscharpf},
pii = {kxq043},
pmcid = {PMC3006124},
pmid = {20625178},
timestamp = {2011.02.26},
url = {http://dx.doi.org/10.1093/biostatistics/kxq043}
|
| 2ae7850e |
@ARTICLE{Carvalho2007a,
author = {Benilton Carvalho and Henrik Bengtsson and Terence P Speed and Rafael
A Irizarry},
title = {Exploration, normalization, and genotype calls of high-density oligonucleotide
SNP array data.},
journal = {Biostatistics},
year = {2007},
volume = {8},
pages = {485--499},
number = {2},
month = {Apr},
abstract = {In most microarray technologies, a number of critical steps are required
to convert raw intensity measurements into the data relied upon by
data analysts, biologists, and clinicians. These data manipulations,
referred to as preprocessing, can influence the quality of the ultimate
measurements. In the last few years, the high-throughput measurement
of gene expression is the most popular application of microarray
technology. For this application, various groups have demonstrated
that the use of modern statistical methodology can substantially
improve accuracy and precision of the gene expression measurements,
relative to ad hoc procedures introduced by designers and manufacturers
of the technology. Currently, other applications of microarrays are
becoming more and more popular. In this paper, we describe a preprocessing
methodology for a technology designed for the identification of DNA
sequence variants in specific genes or regions of the human genome
that are associated with phenotypes of interest such as disease.
In particular, we describe a methodology useful for preprocessing
Affymetrix single-nucleotide polymorphism chips and obtaining genotype
calls with the preprocessed data. We demonstrate how our procedure
improves existing approaches using data from 3 relatively large studies
including the one in which large numbers of independent calls are
available. The proposed methods are implemented in the package oligo
available from Bioconductor.},
doi = {10.1093/biostatistics/kxl042},
institution = {Department of Biostatistics, Johns Hopkins University, Baltimore,
MD 21205, USA.},
keywords = {Algorithms; Alleles; Data Interpretation, Statistical; Genotype; Humans;
Oligonucleotide Array Sequence Analysis; Oligonucleotides; Polymorphism,
Single Nucleotide},
owner = {rscharpf},
pii = {kxl042},
pmid = {17189563},
timestamp = {2008.08.07},
url = {http://dx.doi.org/10.1093/biostatistics/kxl042}
|
