Identification of significant regional genetic variations using continuous CNV values in aCGH data

Missense mutations in the AFG3L2 proteolytic domain account for ∼1.5% of European autosomal dominant cerebellar ataxias

Spinocerebellar ataxia type 28 is an autosomal dominant form of cerebellar ataxia (ADCA) caused by mutations in AFG3L2, a gene that encodes a subunit of the mitochondrial m-AAA protease. We screened 366 primarily Caucasian ADCA families, negative for the most common triplet expansions, for point mutations in AFG3L2 using DHPLC. Whole-gene deletions were excluded in 300 of the patients, and duplications were excluded in 129 patients. We found six missense mutations in nine unrelated index cases (9/366, 2.6%): c.1961C>T (p.Thr654Ile) in exon 15, c.1996A>G (p.Met666Val), c.1997T>G (p.Met666Arg), c.1997T>C (p.Met666Thr), c.2011G>A (p.Gly671Arg), and c.2012G>A (p.Gly671Glu) in exon 16. All mutated amino acids were located in the C-terminal proteolytic domain. In available cases, we demonstrated the mutations segregated with the disease. Mutated amino acids are highly conserved, and bioinformatic analysis indicates the substitutions are likely deleterious. This investigation demonstrates that SCA28 accounts for ∼3% of ADCA Caucasian cases negative for triplet expansions and, in extenso, to ∼1.5% of all ADCA. We further confirm both the involvement of AFG3L2 gene in SCA28 and the presence of a mutational hotspot in exons 15-16. Screening for SCA28, is warranted in patients who test negative for more common SCAs and present with a slowly progressive cerebellar ataxia accompanied by oculomotor signs.

CNAnova: a new approach for finding recurrent copy number abnormalities in cancer SNP microarray data

MOTIVATION

The current generation of single nucleotide polymorphism (SNP) arrays allows measurement of copy number aberrations (CNAs) in cancer at more than one million locations in the genome in hundreds of tumour samples. Most research has focused on single-sample CNA discovery, the so-called segmentation problem. The availability of high-density, large sample-size SNP array datasets makes the identification of recurrent copy number changes in cancer, an important issue that can be addressed using the cross-sample information.

RESULTS

We present a novel approach for finding regions of recurrent copy number aberrations, called CNAnova, from Affymetrix SNP 6.0 array data. The method derives its statistical properties from a control dataset composed of normal samples and, in contrast to previous methods, does not require segmentation and permutation steps. For rigorous testing of the algorithm and comparison to existing methods, we developed a simulation scheme that uses the noise distribution present in Affymetrix arrays. Application of the method to 128 acute lymphoblastic leukaemia samples shows that CNAnova achieves lower error rate than a popular alternative approach. We also describe an extension of the CNAnova framework to identify recurrent CNA regions with intra-tumour heterogeneity, present in either primary or relapsed samples from the same patients.

AVAILABILITY

The CNAnova package and synthetic datasets are available at http://www.compbio.group.cam.ac.uk/software.html.