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1.  Integrative genomics identifies LMO1 as a neuroblastoma oncogene 
Nature  2010;469(7329):216-220.
Neuroblastoma is a childhood cancer of the sympathetic nervous system that accounts for approximately 10% of all paediatric oncology deaths1,2. To identify genetic risk factors for neuroblastoma, we performed a genome-wide association study (GWAS) on 2,251 patients and 6,097 control subjects of European ancestry from four case series. Here we report a significant association within LIM domain only 1 (LMO1) at 11p15.4 (rs110419, combined P = 5.2 × 10−16, odds ratio of risk allele = 1.34 (95% confidence interval 1.25–1.44)). The signal was enriched in the subset of patients with the most aggressive form of the disease. LMO1 encodes a cysteine-rich transcriptional regulator, and its paralogues (LMO2, LMO3 and LMO4) have each been previously implicated in cancer. In parallel, we analysed genome-wide DNA copy number alterations in 701 primary tumours. We found that the LMO1 locus was aberrant in 12.4% through a duplication event, and that this event was associated with more advanced disease (P < 0.0001) and survival (P = 0.041). The germline single nucleotide polymorphism (SNP) risk alleles and somatic copy number gains were associated with increased LMO1 expression in neuroblastoma cell lines and primary tumours, consistent with a gain-of-function role in tumorigenesis. Short hairpin RNA (shRNA)-mediated depletion of LMO1 inhibited growth of neuroblastoma cells with high LMO1 expression, whereas forced expression of LMO1 in neuroblastoma cells with low LMO1 expression enhanced proliferation. These data show that common polymorphisms at the LMO1 locus are strongly associated with susceptibility to developing neuroblastoma, but also may influence the likelihood of further somatic alterations at this locus, leading to malignant progression.
doi:10.1038/nature09609
PMCID: PMC3320515  PMID: 21124317
2.  SNP array mapping of 20p deletions: Genotypes, Phenotypes and Copy Number Variation 
Human mutation  2009;30(3):371-378.
The use of array technology to define chromosome deletions and duplications is bringing us closer to establishing a genotype/phenotype map of genomic copy number alterations. We studied 21 patients and 5 relatives with deletions of the short arm of chromosome 20 using the Illumina HumanHap550 SNP array to 1) more accurately determine the deletion sizes, 2) identify and compare breakpoints, 3) establish genotype/phenotype correlations and 4) investigate the use of the HumanHap550 platform for analysis of chromosome deletions. Deletions ranged from 95kb to 14.62Mb, and all of the breakpoints were unique. Eleven patients had deletions between 95kb and 4Mb and these individuals had normal development, with no anomalies outside of those associated with Alagille syndrome. The proximal and distal boundaries of these eleven deletions constitute a 5.4MB region, and we propose that haploinsufficiency for only 1 of the 12 genes in this region causes phenotypic abnormalities. This defines the JAG1 associated critical region, in which deletions do not confer findings other than those associated with Alagille syndrome. The other 10 patients had deletions between 3.28Mb and 14.62Mb, which extended outside the critical region, and notably, all of these patients, had developmental delay. This group had other findings such as autism, scoliosis and bifid uvula. We identified 47 additional polymorphic genome-wide copy number variants (>20 SNPs), with 0–5 variants called per patient. Deletions of the short arm of chromosome 20 are associated with relatively mild and limited clinical anomalies. The use of SNP arrays provides accurate high-resolution definition of genomic abnormalities.
doi:10.1002/humu.20863
PMCID: PMC2650004  PMID: 19058200
SNP array analysis; 20p deletion; copy number variants; Alagille syndrome; haploinsufficiency; JAG1
3.  Modeling genetic inheritance of copy number variations 
Nucleic Acids Research  2008;36(21):e138.
Copy number variations (CNVs) are being used as genetic markers or functional candidates in gene-mapping studies. However, unlike single nucleotide polymorphism or microsatellite genotyping techniques, most CNV detection methods are limited to detecting total copy numbers, rather than copy number in each of the two homologous chromosomes. To address this issue, we developed a statistical framework for intensity-based CNV detection platforms using family data. Our algorithm identifies CNVs for a family simultaneously, thus avoiding the generation of calls with Mendelian inconsistency while maintaining the ability to detect de novo CNVs. Applications to simulated data and real data indicate that our method significantly improves both call rates and accuracy of boundary inference, compared to existing approaches. We further illustrate the use of Mendelian inheritance to infer SNP allele compositions in each of the two homologous chromosomes in CNV regions using real data. Finally, we applied our method to a set of families genotyped using both the Illumina HumanHap550 and Affymetrix genome-wide 5.0 arrays to demonstrate its performance on both inherited and de novo CNVs. In conclusion, our method produces accurate CNV calls, gives probabilistic estimates of CNV transmission and builds a solid foundation for the development of linkage and association tests utilizing CNVs.
doi:10.1093/nar/gkn641
PMCID: PMC2588508  PMID: 18832372

Results 1-3 (3)