Critical reasoning on causal inference in genome-wide linkage and association studies.

Genome-wide linkage and association studies of tens of thousands of clinical and molecular traits are currently underway, offering rich data for inferring causality between traits and genetic variation. However, the inference process is based on discovering subtle patterns in the correlation between traits and is therefore challenging and could create a flood of untrustworthy causal inferences. Here we introduce the concerns and show that they are already valid in simple scenarios of two traits linked to or associated with the same genomic region. We argue that more comprehensive analysis and Bayesian reasoning are needed and that these can overcome some of the pitfalls, although not in every conceivable case. We conclude that causal inference methods can still be of use in the iterative process of mathematical modeling and biological validation.

DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules.

BACKGROUND: Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns. RESULTS: We present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset. CONCLUSIONS: DiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.

Undifferentiated embryonic cell transcription factor 1 regulates ESC chromatin organization and gene expression.

Previous reports showed that embryonic stem (ES) cells contain hyperdynamic and globally transcribed chromatin-properties that are important for ES cell pluripotency and differentiation. Here, we demonstrate a role for undifferentiated embryonic cell transcription factor 1 (UTF1) in regulating ES cell chromatin structure. Using chromatin immunoprecipitation-on-chip analysis, we identified >1,700 UTF1 target genes that significantly overlap with previously identified Nanog, Oct4, Klf-4, c-Myc, and Rex1 targets. Gene expression profiling showed that UTF1 knock down results in increased expression of a large set of genes, including a significant number of UTF1 targets. UTF1 knock down (KD) ES cells are, irrespective of the increased expression of several self-renewal genes, Leukemia inhibitory factor (LIF) dependent. However, UTF1 KD ES cells are perturbed in their differentiation in response to dimethyl sulfoxide (DMSO) or after LIF withdrawal and display increased colony formation. UTF1 KD ES cells display extensive chromatin decondensation, reflected by a dramatic increase in nucleosome release on micrococcal nuclease (MNase) treatment and enhanced MNase sensitivity of UTF1 target genes in UTF1 KD ES cells. Summarizing, our data show that UTF1 is a key chromatin component in ES cells, preventing ES cell chromatin decondensation, and aberrant gene expression; both essential for proper initiation of lineage-specific differentiation of ES cells.

XGAP: a uniform and extensible data model and software platform for genotype and phenotype experiments.

We present an extensible software model for the genotype and phenotype community, XGAP. Readers can download a standard XGAP (http://www.xgap.org) or auto-generate a custom version using MOLGENIS with programming interfaces to R-software and web-services or user interfaces for biologists. XGAP has simple load formats for any type of genotype, epigenotype, transcript, protein, metabolite or other phenotype data. Current functionality includes tools ranging from eQTL analysis in mouse to genome-wide association studies in humans.

Expression quantitative trait loci are highly sensitive to cellular differentiation state.

Genetical genomics is a strategy for mapping gene expression variation to expression quantitative trait loci (eQTLs). We performed a genetical genomics experiment in four functionally distinct but developmentally closely related hematopoietic cell populations isolated from the BXD panel of recombinant inbred mouse strains. This analysis allowed us to analyze eQTL robustness/sensitivity across different cellular differentiation states. Although we identified a large number (365) of "static" eQTLs that were consistently active in all four cell types, we found a much larger number (1,283) of "dynamic" eQTLs showing cell-type-dependence. Of these, 140, 45, 531, and 295 were preferentially active in stem, progenitor, erythroid, and myeloid cells, respectively. A detailed investigation of those dynamic eQTLs showed that in many cases the eQTL specificity was associated with expression changes in the target gene. We found no evidence for target genes that were regulated by distinct eQTLs in different cell types, suggesting that large-scale changes within functional regulatory networks are uncommon. Our results demonstrate that heritable differences in gene expression are highly sensitive to the developmental stage of the cell population under study. Therefore, future genetical genomics studies should aim at studying multiple well-defined and highly purified cell types in order to construct as comprehensive a picture of the changing functional regulatory relationships as possible.

eQTL analysis in mice and rats.

Since the introduction of genetical genomics in 2001, many studies have been published on various organisms, including mouse and rat. Genetical genomics makes use of the latest microarray profiling technologies and combines vast amounts of genotype and gene expression information, a strategy that has proven very successful in inbred line crosses. The data are analyzed using standard tools for linkage analysis to map the genetic determinants of gene expression variation. Typically, studies have singled out hundreds of genomic loci regulating the expression of nearby and distant genes (called local and distant expression quantitative trait loci, respectively; eQTLs). In this chapter, we provide a step-by-step guide to performing genome-wide linkage analysis in an eQTL mapping experiment by using the R statistical software framework.

Defining gene and QTL networks.

Current technologies for high-throughput molecular profiling of large numbers of genetically different individuals offer great potential for elucidating the genotype-to-phenotype relationship. Variation in molecular and phenotypic traits can be correlated to DNA sequence variation using the methods of quantitative trait locus (QTL) mapping. In addition, the correlation structure in the molecular and phenotypic traits can be informative for inferring the underlying molecular networks. For this, new methods are emerging to distinguish among causality, reactivity, or independence of traits based upon logic involving underlying QTL. These methods are becoming increasingly popular in plant genetic studies as well as in studies on many other organisms.