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DNA methylation is critical for normal development and plays important roles in genome organization and transcriptional regulation. Although DNA methyltransferases have been identified, the factors that establish and contribute to genome-wide methylation patterns remain elusive. Here, we report a high-resolution cytosine methylation map of the murine genome modulated by Lsh, a chromatin remodeling family member that has previously been shown to regulate CpG methylation at repetitive sequences. We provide evidence that Lsh also controls genome-wide cytosine methylation at nonrepeat sequences and relate those changes to alterations in H4K4me3 modification and gene expression. Deletion of Lsh alters the allocation of cytosine methylation in chromosomal regions of 50 kb to 2 Mb and, in addition, leads to changes in the methylation profile at the 5′ end of genes. Furthermore, we demonstrate that loss of Lsh promotes—as well as prevents—cytosine methylation. Our data indicate that Lsh is an epigenetic modulator that is critical for normal distribution of cytosine methylation throughout the murine genome.

The genetic case-control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two-stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p-value that controls a familywise error rate. In the second stage, summary p-values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature.

Background: One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself.