Genome-wide analysis of DNA copy-number changes using cDNA microarrays.

Gene amplifications and deletions frequently contribute to tumorigenesis. Characterization of these DNA copy-number changes is important for both the basic understanding of cancer and its diagnosis. Comparative genomic hybridization (CGH) was developed to survey DNA copy-number variations across a whole genome. With CGH, differentially labelled test and reference genomic DNAs are co-hybridized to normal metaphase chromosomes, and fluorescence ratios along the length of chromosomes provide a cytogenetic representation of DNA copy-number variation. CGH, however, has a limited ( approximately 20 Mb) mapping resolution, and higher-resolution techniques, such as fluorescence in situ hybridization (FISH), are prohibitively labour-intensive on a genomic scale. Array-based CGH, in which fluorescence ratios at arrayed DNA elements provide a locus-by-locus measure of DNA copy-number variation, represents another means of achieving increased mapping resolution. Published array CGH methods have relied on large genomic clone (for example BAC) array targets and have covered only a small fraction of the human genome. cDNAs representing over 30,000 radiation-hybrid (RH)-mapped human genes provide an alternative and readily available genomic resource for mapping DNA copy-number changes. Although cDNA microarrays have been used extensively to characterize variation in human gene expression, human genomic DNA is a far more complex mixture than the mRNA representation of human cells. Therefore, analysis of DNA copy-number variation using cDNA microarrays would require a sensitivity of detection an order of magnitude greater than has been routinely reported. We describe here a cDNA microarray-based CGH method, and its application to DNA copy-number variation analysis in breast cancer cell lines and tumours. Using this assay, we were able to identify gene amplifications and deletions genome-wide and with high resolution, and compare alterations in DNA copy number and gene expression.     

Identifying distinct classes of bladder carcinoma using microarrays

Bladder cancer is a common malignant disease characterized by frequent recurrences. The stage of disease at diagnosis and the presence of surrounding carcinoma in situ are important in determining the disease course of an affected individual. Despite considerable effort, no accepted immunohistological or molecular markers have been identified to define clinically relevant subsets of bladder cancer. Here we report the identification of clinically relevant subclasses of bladder carcinoma using expression microarray analysis of 40 well characterized bladder tumors. Hierarchical cluster analysis identified three major stages, Ta, T1 and T2-4, with the Ta tumors further classified into subgroups. We built a 32-gene molecular classifier using a cross-validation approach that was able to classify benign and muscle-invasive tumors with close correlation to pathological staging in an independent test set of 68 tumors. The classifier provided new predictive information on disease progression in Ta tumors compared with conventional staging (P < 0.005). To delineate non-recurring Ta tumors from frequently recurring Ta tumors, we analyzed expression patterns in 31 tumors by applying a supervised learning classification methodology, which classified 75% of the samples correctly (P < 0.006). Furthermore, gene expression profiles characterizing each stage and subtype identified their biological properties, producing new potential targets for therapy.     

Mining the human genome using microarrays of open reading frames

To test the hypothesis that the human genome project will uncover many genes not previously discovered by sequencing of expressed sequence tags (ESTs), we designed and produced a set of microarrays using probes based on open reading frames (ORFs) in 350 Mb of finished and draft human sequence. Our approach aims to identify all genes directly from genomic sequence by querying gene expression. We analysed genomic sequence with a suite of ORF prediction programs, selected approximately one ORF per gene, amplified the ORFs from genomic DNA and arrayed the amplicons onto treated glass slides. Of the first 10,000 arrayed ORFs, 31% are completely novel and 29% are similar, but not identical, to sequences in public databases. Approximately one-half of these are expressed in the tissues we queried by microarray. Subsequent verification by other techniques confirmed expression of several of the novel genes. Expressed sequence tags (ESTs) have yielded vast amounts of data, but our results indicate that many genes in the human genome will only be found by genomic sequencing.     

Genome-wide analysis of single-nucleotide polymorphisms in human expressed sequences

Single-nucleotide polymorphisms (SNPs) have been explored as a high-resolution marker set for accelerating the mapping of disease genes. Here we report 48,196 candidate SNPs detected by statistical analysis of human expressed sequence tags (ESTs), associated primarily with coding regions of genes. We used Bayesian inference to weigh evidence for true polymorphism versus sequencing error, misalignment or ambiguity, misclustering or chimaeric EST sequences, assessing data such as raw chromatogram height, sharpness, overlap and spacing, sequencing error rates, context-sensitivity and cDNA library origin. Three separate validations-comparison with 54 genes screened for SNPs independently, verification of HLA-A polymorphisms and restriction fragment length polymorphism (RFLP) testing-verified 70%, 89% and 71% of our predicted SNPs, respectively. Our method detects tenfold more true HLA-A SNPs than previous analyses of the EST data. We found SNPs in a large fraction of known disease genes, including some disease-causing mutations (for example, the HbS sickle-cell mutation). Our comprehensive analysis of human coding region polymorphism provides a public resource for mapping of disease genes (available at http://www.bioinformatics.ucla.edu/snp).     

Genome-wide association study and mouse model identify interaction between RET and EDNRB pathways in Hirschsprung disease

Genetic studies of Hirschsprung disease, a common congenital malformation, have identified eight genes with mutations that can be associated with this condition. Mutations at individual loci are, however, neither necessary nor sufficient to cause clinical disease. We conducted a genome-wide association study in 43 Mennonite family trios using 2,083 microsatellites and single-nucleotide polymorphisms and a new multipoint linkage disequilibrium method that searches for association arising from common ancestry. We identified susceptibility loci at 10q11, 13q22 and 16q23; the gene at 13q22 is EDNRB, encoding a G protein-coupled receptor (GPCR) and the gene at 10q11 is RET, encoding a receptor tyrosine kinase (RTK). Statistically significant joint transmission of RET and EDNRB alleles in affected individuals and non-complementation of aganglionosis in mouse intercrosses between Ret null and the Ednrb hypomorphic piebald allele are suggestive of epistasis between EDNRB and RET. Thus, genetic interaction between mutations in RET and EDNRB is an underlying mechanism for this complex disorder.     

Genome-wide functional analysis of pathogenicity genes in the rice blast fungus

Rapid translation of genome sequences into meaningful biological information hinges on the integration of multiple experimental and informatics methods into a cohesive platform. Despite the explosion in the number of genome sequences available, such a platform does not exist for filamentous fungi. Here we present the development and application of a functional genomics and informatics platform for a model plant pathogenic fungus, Magnaporthe oryzae. In total, we produced 21,070 mutants through large-scale insertional mutagenesis using Agrobacterium tumefaciens-mediated transformation. We used a high-throughput phenotype screening pipeline to detect disruption of seven phenotypes encompassing the fungal life cycle and identified the mutated gene and the nature of mutation for each mutant. Comparative analysis of phenotypes and genotypes of the mutants uncovered 202 new pathogenicity loci. Our findings demonstrate the effectiveness of our platform and provide new insights on the molecular basis of fungal pathogenesis. Our approach promises comprehensive functional genomics in filamentous fungi and beyond.     

De novo assembly and genotyping of variants using colored de Bruijn graphs

Detecting genetic variants that are highly divergent from a reference sequence remains a major challenge in genome sequencing. We introduce de novo assembly algorithms using colored de Bruijn graphs for detecting and genotyping simple and complex genetic variants in an individual or population. We provide an efficient software implementation, Cortex, the first de novo assembler capable of assembling multiple eukaryotic genomes simultaneously. Four applications of Cortex are presented. First, we detect and validate both simple and complex structural variations in a high-coverage human genome. Second, we identify more than 3 Mb of sequence absent from the human reference genome, in pooled low-coverage population sequence data from the 1000 Genomes Project. Third, we show how population information from ten chimpanzees enables accurate variant calls without a reference sequence. Last, we estimate classical human leukocyte antigen (HLA) genotypes at HLA-B, the most variable gene in the human genome.     

Epigenome analyses using BAC microarrays identify evolutionary conservation of tissue-specific methylation of SHANK3

CpG islands are present in one-half of all human and mouse genes and typically overlap with promoters or exons. We developed a method for high-resolution analysis of the methylation status of CpG islands genome-wide, using arrays of BAC clones and the methylation-sensitive restriction enzyme NotI. Here we demonstrate the accuracy and specificity of the method. By computationally mapping all NotI sites, methylation events can be defined with single-nucleotide precision throughout the genome. We also demonstrate the unique expandability of the array method using a different methylation-sensitive restriction enzyme, BssHII. We identified and validated new CpG island loci that are methylated in a tissue-specific manner in normal human tissues. The methylation status of the CpG islands is associated with gene expression for several genes, including SHANK3, which encodes a structural protein in neuronal postsynaptic densities. Defects in SHANK3 seem to underlie human 22q13 deletion syndrome. Furthermore, these patterns for SHANK3 are conserved in mice and rats.     

De novo deletions of SNRPN exon 1 in early human and mouse embryos result in a paternal to maternal imprint switch

Prader-Willi syndrome (PWS) is a neurogenetic disease characterized by infantile hypotonia, gonadal hypoplasia, obsessive behaviour and neonatal feeding difficulties followed by hyperphagia, leading to profound obesity. PWS is due to a lack of paternal genetic information at 15q11-q13 (ref. 2). Five imprinted, paternally expressed genes map to the PWS region, MKRN3 (ref. 3), NDN (ref. 4), NDNL1 (ref. 5), SNRPN (refs 6-8 ) and IPW (ref. 9), as well as two poorly characterized framents designated PAR-1 and PAR-5 (ref. 10). Imprinting of this region involves a bipartite 'imprinting centre' (IC), which overlaps SNRPN (refs 10,11). Deletion of the SNRPN promoter/exon 1 region (the PWS IC element) appears to impair the establishment of the paternal imprint in the male germ line and leads to PWS. Here we report a PWS family in which the father is mosaic for an IC deletion on his paternal chromosome. The deletion chromosome has acquired a maternal methylation imprint in his somatic cells. We have made identical findings in chimaeric mice generated from two independent embryonic stem (ES) cell lines harbouring a similar deletion. Our studies demonstrate that the PWS IC element is not only required for the establishment of the paternal imprint, but also for its postzygotic maintenance.