A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data.

BACKGROUND: Several statistical tests have been developed for analyzing genome-wide association data by incorporating gene pathway information in terms of gene sets. Using these methods, hundreds of gene sets are typically tested, and the tested gene sets often overlap. This overlapping greatly increases the probability of generating false positives, and the results obtained are difficult to interpret, particularly when many gene sets show statistical significance. RESULTS: We propose a flexible statistical framework to circumvent these problems. Inspired by spatial scan statistics for detecting clustering of disease occurrence in the field of epidemiology, we developed a scan statistic to extract disease-associated gene clusters from a whole gene pathway. Extracting one or a few significant gene clusters from a global pathway limits the overall false positive probability, which results in increased statistical power, and facilitates the interpretation of test results. In the present study, we applied our method to genome-wide association data for rare copy-number variations, which have been strongly implicated in common diseases. Application of our method to a simulated dataset demonstrated the high accuracy of this method in detecting disease-associated gene clusters in a whole gene pathway. CONCLUSIONS: The scan statistic approach proposed here shows a high level of accuracy in detecting gene clusters in a whole gene pathway. This study has provided a sound statistical framework for analyzing genome-wide rare CNV data by incorporating topological information on the gene pathway.

Major contribution of dominant inheritance to autism spectrum disorders (ASDs) in population-based families.

Results of twin studies have shown that autism spectrum disorders (ASDs) are attributable to complex multigenic interactions rather than to a single susceptibility gene. However, the growing number of distinct, individually rare genetic causes of ASDs, mostly copy number variations (CNVs), favors an alternative to the polygenic hypothesis, the two-component model, which suggests that ASDs are caused either by de novo mutation or by dominant inheritance from asymptomatic carriers of such a mutation. To verify this hypothesis, we estimated the distribution of ASD-risk among both catchment area-based families and multiplex families. Our results suggest that the models with more than three risk components are preferable to the two-component model. Our results also suggest that the largest proportion of ASD cases is caused by dominant inheritance. We additionally show that Supplementary information regarding prevalence has a crucial role in analyzing proband-ascertained data.

Diverse p53 gene aberration in hepatocellular carcinoma detected by dual-color fluorescence in situ hybridization.

BACKGROUND AND AIM: In order to evaluate loss of the p53 gene more precisely, we performed dual-color fluorescence in situ hybridization (dual-color FISH) for chromosome 17 and p53 gene together with DNA polymorphism analysis of the p53 gene in hepatocellular carcinoma (HCC). METHODS: Dual-color FISH using probes specific for the centromere of chromosome 17 and the p53 gene was performed for 41 HCC and DNA polymorphism analysis was also performed for them. RESULTS: Of the 34 HCC tested by dual-color FISH, 20 had loss of at least one p53 gene (58.8%). In contrast, of the 32 HCC tested by DNA polymorphism analysis, 23 gave informative results, among which only eight had loss of heterozygosity (LOH) of the p53 gene (34.8%). Notably, among 14 cases positive for loss of the p53 gene by dual-color FISH, seven cases were negative for LOH of the p53 gene. Moreover, dual-color FISH revealed that the percentage of cells that lost at least one p53 gene increased as the HCC became less differentiated (P < 0.01), whereas LOH did not reveal any such correlation. CONCLUSIONS: These data suggest that loss of the p53 gene was present in a considerable number of HCC, and diversity of the p53 gene aberration increases with progression of HCC. Dual-color FISH is an effective method for detection of p53 gene aberration, and it can provide new insight into oncogenesis in HCC.