Gary A. Churchill

R/qtl: QTL mapping in experimental crosses.

SUMMARY R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental populations derived from inbred lines. It is implemented as an add-on package for the freely-available statistical software, R, and includes functions for estimating genetic maps, identifying genotyping errors, and performing single-QTL and two-dimensional, two-QTL genome scans by multiple methods, with the possible inclusion of covariates. AVAILABILITY The package is freely available at http://www.biostat.jhsph.edu/~kbroman/qtl.

Fundamentals of experimental design for cDNA microarrays

Microarray technology is now widely available and is being applied to address increasingly complex scientific questions. Consequently, there is a greater demand for statistical assessment of the conclusions drawn from microarray experiments. This review discusses fundamental issues of how to design an experiment to ensure that the resulting data are amenable to statistical analysis. The discussion focuses on two-color spotted cDNA microarrays, but many of the same issues apply to single-color gene-expression assays as well.

Characterization of human embryonic stem cell lines by the International Stem Cell Initiative

The International Stem Cell Initiative characterized 59 human embryonic stem cell lines from 17 laboratories worldwide. Despite diverse genotypes and different techniques used for derivation and maintenance, all lines exhibited similar expression patterns for several markers of human embryonic stem cells. They expressed the glycolipid antigens SSEA3 and SSEA4, the keratan sulfate antigens TRA-1-60, TRA-1-81, GCTM2 and GCT343, and the protein antigens CD9, Thy1 (also known as CD90), tissue-nonspecific alkaline phosphatase and class 1 HLA, as well as the strongly developmentally regulated genes NANOG, POU5F1 (formerly known as OCT4), TDGF1, DNMT3B, GABRB3 and GDF3. Nevertheless, the lines were not identical: differences in expression of several lineage markers were evident, and several imprinted genes showed generally similar allele-specific expression patterns, but some gene-dependent variation was observed. Also, some female lines expressed readily detectable levels of XIST whereas others did not. No significant contamination of the lines with mycoplasma, bacteria or cytopathic viruses was detected.

Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors

BackgroundAlthough numerous mouse models of breast carcinomas have been developed, we do not know the extent to which any faithfully represent clinically significant human phenotypes. To address this need, we characterized mammary tumor gene expression profiles from 13 different murine models using DNA microarrays and compared the resulting data to those from human breast tumors.ResultsUnsupervised hierarchical clustering analysis showed that six models (TgWAP-Myc, TgMMTV-Neu, TgMMTV-PyMT, TgWAP-Int3, TgWAP-Tag, and TgC3(1)-Tag) yielded tumors with distinctive and homogeneous expression patterns within each strain. However, in each of four other models (TgWAP-T121, TgMMTV-Wnt1, Brca1Co/Co;TgMMTV-Cre;p53+/- and DMBA-induced), tumors with a variety of histologies and expression profiles developed. In many models, similarities to human breast tumors were recognized, including proliferation and human breast tumor subtype signatures. Significantly, tumors of several models displayed characteristics of human basal-like breast tumors, including two models with induced Brca1 deficiencies. Tumors of other murine models shared features and trended towards significance of gene enrichment with human luminal tumors; however, these murine tumors lacked expression of estrogen receptor (ER) and ER-regulated genes. TgMMTV-Neu tumors did not have a significant gene overlap with the human HER2+/ER- subtype and were more similar to human luminal tumors.ConclusionMany of the defining characteristics of human subtypes were conserved among the mouse models. Although no single mouse model recapitulated all the expression features of a given human subtype, these shared expression features provide a common framework for an improved integration of murine mammary tumor models with human breast tumors.

Statistical tests for differential expression in cDNA microarray experiments

Extracting biological information from microarray data requires appropriate statistical methods. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation.

Variation in gene expression within and among natural populations

Evolution may depend more strongly on variation in gene expression than on differences between variant forms of proteins. Regions of DNA that affect gene expression are highly variable, containing 0.6% polymorphic sites. These naturally occurring polymorphic nucleotides can alter in vivo transcription rates. Thus, one might expect substantial variation in gene expression between individuals. But the natural variation in mRNA expression for a large number of genes has not been measured. Here we report microarray studies addressing the variation in gene expression within and between natural populations of teleost fish of the genus Fundulus. We observed statistically significant differences in expression between individuals within the same population for approximately 18% of 907 genes. Expression typically differed by a factor of 1.5, and often more than 2.0. Differences between populations increased the variation. Much of the variation between populations was a positive function of the variation within populations and thus is most parsimoniously described as random. Some genes showed unexpected patterns of expression—changes unrelated to evolutionary distance. These data suggest that substantial natural variation exists in gene expression and that this quantitative variation is important in evolution.

Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments

We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization technique, bootstrapping. Bootstrapping has previously been used to obtain confidence intervals for estimates of differential expression for individual genes. Here we apply bootstrapping to assess the stability of results from a cluster analysis. We illustrate the technique with a publicly available data set and draw conclusions about the reliability of clustering results in light of variation in the data. The bootstrapping procedure relies on experimental replication. We discuss the implications of replication and good design in microarray experiments.

The nature and identification of quantitative trait loci: a community’s view

This white paper by eighty members of the Complex Trait Consortium presents a communitys view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

Stochastic models for heterogeneous DNA sequences

The composition of naturally occurring DNA sequences is often strikingly heterogeneous. In this paper, the DNA sequence is viewed as a stochastic process with local compositional properties determined by the states of a hidden Markov chain. The model used is a discrete-state, discrete-outcome version of a general model for non-stationary time series proposed by Kitagawa (1987). A smoothing algorithm is described which can be used to reconstruct the hidden process and produce graphic displays of the compositional structure of a sequence. The problem of parameter estimation is approached using likelihood methods and an EM algorithm for approximating the maximum likelihood estimate is derived. The methods are applied to sequences from yeast mitochondrial DNA, human and mouse mitochondrial DNAs, a human X chromosomal fragment and the complete genome of bacteriophage lambda.

MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments

We describe a software package called MAANOVA (MicroArray ANalysis Of VAriance). MAANOVA is a collection of functions for statistical analysis of gene expression data from two-color cDNA microarray experiments. It is available in both the Matlab and R programming environments and can be run on any platform that supports these packages. MAANOVA allows the user to assess data quality, apply data transformations, estimate relative gene expression from designed experiments with ANOVA models, evaluate and interpret ANOVA models, formally test for differential expression of genes and estimate false-discovery rates, produce graphical summaries of expression patterns, and perform cluster analysis with bootstrapping. The development of MAANOVA was motivated by the need to analyze microarray data that arise from sophisticated designed experiments. MAANOVA provides specialized functions for microarray analysis in an open-ended format within flexible computing environments. MAANOVA functions can be used alone or in co mbination with other functions for the rigorous statistical analysis of microarray data.