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1.  A high resolution HLA and SNP haplotype map for disease association studies in the extended human MHC 
Nature genetics  2006;38(10):1166-1172.
The proteins encoded by the classical HLA class I and class II genes in the major histocompatibility complex (MHC) are highly polymorphic and play an essential role in self/non-self immune recognition. HLA variation is a crucial determinant of transplant rejection and susceptibility to a large number of infectious and autoimmune disease1. Yet identification of causal variants is problematic due to linkage disequilibrium (LD) that extends across multiple HLA and non-HLA genes in the MHC2,3. We therefore set out to characterize the LD patterns between the highly polymorphic HLA genes and background variation by typing the classical HLA genes and >7,500 common single nucleotide polymorphisms (SNPs) and deletion/insertion polymorphisms (DIPs) across four population samples. The analysis provides informative tag SNPs that capture some of the variation in the MHC region and that could be used in initial disease association studies, and provides new insight into the evolutionary dynamics and ancestral origins of the HLA loci and their haplotypes.
doi:10.1038/ng1885
PMCID: PMC2670196  PMID: 16998491
2.  A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis 
Nature biotechnology  2008;26(7):779-785.
DNA methylation is an indispensible epigenetic modification of mammalian genomes. Consequently there is great interest in strategies for genome-wide/whole-genome DNA methylation analysis, and immunoprecipitation-based methods have proven to be a powerful option. Such methods are rapidly shifting the bottleneck from data generation to data analysis, necessitating the development of better analytical tools. Until now, a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling has been the inability to estimate absolute methylation levels. Here we report the development of a novel cross-platform algorithm – Bayesian Tool for Methylation Analysis (Batman) – for analyzing Methylated DNA Immunoprecipitation (MeDIP) profiles generated using arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). The latter is an approach we have developed to elucidate the first high-resolution whole-genome DNA methylation profile (DNA methylome) of any mammalian genome. MeDIP-seq/MeDIP-chip combined with Batman represent robust, quantitative, and cost-effective functional genomic strategies for elucidating the function of DNA methylation.
doi:10.1038/nbt1414
PMCID: PMC2644410  PMID: 18612301

Results 1-2 (2)