Dnmt3a is essential for hematopoietic stem cell differentiation

Loss of the de novo DNA methyltransferases Dnmt3a and Dnmt3b in embryonic stem cells obstructs differentiation; however, the role of these enzymes in somatic stem cells is largely unknown. Using conditional ablation, we show that Dnmt3a loss progressively impairs hematopoietic stem cell (HSC) differentiation over serial transplantation, while simultaneously expanding HSC numbers in the bone marrow. Dnmt3a-null HSCs show both increased and decreased methylation at distinct loci, including substantial CpG island hypermethylation. Dnmt3a-null HSCs upregulate HSC multipotency genes and downregulate differentiation factors, and their progeny exhibit global hypomethylation and incomplete repression of HSC-specific genes. These data establish Dnmt3a as a critical participant in the epigenetic silencing of HSC regulatory genes, thereby enabling efficient differentiation.     

The roads from phenotypic variation to gene discovery: mutagenesis versus QTLs

In model organisms, chemical mutagenesis provides a powerful alternative to natural, polygenic variation (for example, quantitative trait loci (QTLs)) for identifying functional pathways and complex disease genes. Despite recent progress in QTLs, we expect that mutagenesis is will ultimately prove more effective because the prospects of gene identification are high and every gene affecting a trait is potentially a target.     

Genome-wide genetic association of complex traits in heterogeneous stock mice

Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.     

Functional screening of an asthma QTL in YAC transgenic mice.

Many quantitative trait loci (QTLs) contributing to genetically complex conditions have been discovered, but few causative genes have been identified. This is mainly due to the large size of QTLs and the subtle connection between genotype and quantitative phenotype associated with these conditions. Transgenic mice have been successfully used to analyse well-characterized genes suspected of contributing to quantitative traits. Although this approach is powerful for examining one gene at a time, it can be impractical for surveying the large genomic intervals containing many genes that are typically associated with QTLs. To screen for genes contributing to an asthma QTL mapped to human chromosome 5q3 (refs 6,7), we characterized a panel of large-insert 5q31 transgenics based on studies demonstrating that altering gene dosage frequently affects quantitative phenotypes normally influenced by that gene. This panel of human YAC transgenics, propagating a 1-Mb interval of chromosome 5q31 containing 6 cytokine genes and 17 partially characterized genes, was screened for quantitative changes in several asthma-associated phenotypes. Multiple independent transgenic lines with altered IgE response to antigen treatment shared a 180-kb region containing 5 genes, including those encoding human interleukin 4 (IL4) and interleukin 13 (IL13 ), which induce IgE class switching in B cells. Further analysis of these mice and mice transgenic for mouse Il4 and Il13 demonstrated that moderate changes in Il4 and Il13 expression affect asthma-associated phenotypes in vivo. This functional screen of large-insert transgenics enabled us to identify genes that influence the QTL phenotype in vivo.     

Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.     

Reverse engineering of regulatory networks in human B cells

Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.     

Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population

Nested association mapping (NAM) offers power to resolve complex, quantitative traits to their causal loci. The maize NAM population, consisting of 5,000 recombinant inbred lines (RILs) from 25 families representing the global diversity of maize, was evaluated for resistance to southern leaf blight (SLB) disease. Joint-linkage analysis identified 32 quantitative trait loci (QTLs) with predominantly small, additive effects on SLB resistance. Genome-wide association tests of maize HapMap SNPs were conducted by imputing founder SNP genotypes onto the NAM RILs. SNPs both within and outside of QTL intervals were associated with variation for SLB resistance. Many of these SNPs were within or near sequences homologous to genes previously shown to be involved in plant disease resistance. Limited linkage disequilibrium was observed around some SNPs associated with SLB resistance, indicating that the maize NAM population enables high-resolution mapping of some genome regions.     

Epigenetic status of human embryonic stem cells

We examined the allele-specific expression of six imprinted genes and the methylation profiles of three imprinting control regions to assess the epigenetic status of human embryonic stem cells. We identified generally monoallelic gene expression and normal methylation patterns. During prolonged passage, one cell line became biallelic with respect to H19, but without loss of the gametic methylation imprint. These data argue for a substantial degree of epigenetic stability in human embryonic stem cells.     

Fine mapping of regulatory loci for mammalian gene expression using radiation hybrids

We mapped regulatory loci for nearly all protein-coding genes in mammals using comparative genomic hybridization and expression array measurements from a panel of mouse-hamster radiation hybrid cell lines. The large number of breaks in the mouse chromosomes and the dense genotyping of the panel allowed extremely sharp mapping of loci. As the regulatory loci result from extra gene dosage, we call them copy number expression quantitative trait loci, or ceQTLs. The -2log10P support interval for the ceQTLs was <150 kb, containing an average of <2-3 genes. We identified 29,769 trans ceQTLs with -log10P > 4, including 13 hotspots each regulating >100 genes in trans. Further, this work identifies 2,761 trans ceQTLs harboring no known genes, and provides evidence for a mode of gene expression autoregulation specific to the X chromosome.