In 1967, Cross et al. [J Pediatr 1967;70:398-406] reported four siblings with intellectual disability, microcephaly, neurologic and ocular disorders, and hypopigmentation involving skin and hair. This novel entity, known as oculocerebral hypopigmentation syndrome (OCHS) or Cross syndrome (OMIM 257800), is assumed to be autosomal recessive. However, its genetic cause is still unknown.
A 4-year-old girl is reported with OCHS, a history of recurrent infections and vertebral fusion of L4-L5. Central nervous system and cardiac imaging as well as metabolic screening were normal. Microscopic hair investigations did not show any melanin deposit defects.
Using molecular cytogenetics, we detected a de novo interstitial del(3)(q27.1q29) of the paternal chromosome. To our knowledge, this is the first molecular genetics finding in a patient with OCHS. Here we discuss the genotype-phenotype correlations and suggest candidate genes for this disorder.
Investigating further patients would enable fine-mapping the OCHS locus and identifying its putative gene.
[Show abstract][Hide abstract] ABSTRACT: In this opinion piece, we attempt to unify recent arguments we have made that serious confounds affect the use of network data to predict and characterize gene function. The development of computational approaches to determine gene function is a major strand of computational genomics research. However, progress beyond using BLAST to transfer annotations has been surprisingly slow. We have previously argued that a large part of the reported success in using "guilt by association" in network data is due to the tendency of methods to simply assign new functions to already well-annotated genes. While such predictions will tend to be correct, they are generic; it is true, but not very helpful, that a gene with many functions is more likely to have any function. We have also presented evidence that much of the remaining performance in cross-validation cannot be usefully generalized to new predictions, making progressive improvement in analysis difficult to engineer. Here we summarize our findings about how these problems will affect network analysis, discuss some ongoing responses within the field to these issues, and consolidate some recommendations and speculation, which we hope will modestly increase the reliability and specificity of gene function prediction.
F1000 Research 09/2012; 1. DOI:10.12688/f1000research.1-14.v1
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