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Am J Med Genet A. 2013 Nov;161A(11):2909-19. doi: 10.1002/ajmg.a.36252. Epub 2013 Oct 2.

Cornelia de Lange individuals with new and recurrent SMC1A mutations enhance delineation of mutation repertoire and phenotypic spectrum.

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Department of Health Sciences, Medical Genetics, Università degli Studi di Milano, Milan, Italy.


We report on the clinical and molecular characterization of eight patients, one male and seven females, with clinical diagnosis of Cornelia de Lange syndrome (CdLS), who were found to carry distinct mutations of the SMC1A gene. Five of the eight mutations are novel, with two involving amino acid residues previously described as altered in a different way. The other three have been reported each in a single case. Comparison of pairs of individuals with the same mutation indicates only partial overlap of their clinical phenotypes. The following novel missense mutations, all affecting highly conserved amino acid residues, were found: p.R398G in the N-terminal coiled-coil domain, p.V651M in the C-terminal coiled-coil/hinge junction, p.R693G in the C-terminal coiled-coil, and p.N1166T and p.L1189F in the C-terminal ABC cassette. The latter is localized in the H-loop, and represents the first mutation involving a functional motif of SMC1A protein. The effect of the mutations on SMC1A protein function has been predicted using four bioinformatic tools. All mutations except p.V651M were scored as pathogenic by three or four of the tools. p.V651M was found in the only male individual of our cohort, who presented with the most severe phenotype. This raises the issue of gender effect when addressing mutation-phenotype correlation for genes such as SMC1A, which incompletely escapes X-inactivation. Our clinical and molecular findings expand the total number of characterized SMC1A-mutated patients (from 44 to 52) and the restricted repertoire of SMC1A mutations (from 29 to 34), contributing to the molecular and clinical signature of SMC1A-based CdLS.


Cornelia de Lange syndrome; SMC1A mutations; gender effect; genotype-phenotype correlations; in silico prediction tools

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