Neurofibromatosis type I: mutation spectrum of NF1 in spanish patients.

Neurofibromatosis type I (NF1) is one of the most common genetic disorders in humans. NF1, a tumor predisposition syndrome, is caused by heterozygous pathogenic variants in the NF1 gene. Molecular genetic testing of NF1 is complex, especially because of the presence of a high number of partial pseudogenes, some of them with a high percentage of sequence identity. In this study, we have analyzed the largest cohort of NF1 Spanish patients (150 unrelated individuals suspected of having NF1 and 53 relatives, making a total of 203 individuals). Mutation analysis of the entire coding region was performed in all unrelated index patients. Additionally, the Multiplex Ligation-dependent Probe Amplification (MLPA) test of the NF1 gene and SPRED1 gene analysis (sequencing and MLPA test) was performed in some of the negative patients for NF1 point mutations. When fulfilling the National Institutes of Health (NIH) criterion for the clinical diagnosis of NF1, the detection rate was 79%. Among the 80 genetically confirmed NF1 probands, we detected 69 different pathogenic variants. Two mutations (3%) were gross deletions of the whole gene, the remaining 78 mutations (97%) were small changes spread among all NF1 exons. Among these 69 different mutations detected, 42 mutations were described elsewhere, and 27 mutations were novel mutations. When segregation was studied, 67% of mutations resulted de novo variants. No genetic mosaicism was detected on patients' parents.

X-Linked and Autosomal Recessive Alport Syndrome: Pathogenic Variant Features and Further Genotype-Phenotype Correlations.

Alport syndrome results from mutations in the COL4A5 (X-linked) or COL4A3/COL4A4 (recessive) genes. This study examined 754 previously- unpublished variants in these genes from individuals referred for genetic testing in 12 accredited diagnostic laboratories worldwide, in addition to all published COL4A5, COL4A3 and COL4A4 variants in the LOVD databases. It also determined genotype-phenotype correlations for variants where clinical data were available. Individuals were referred for genetic testing where Alport syndrome was suspected clinically or on biopsy (renal failure, hearing loss, retinopathy, lamellated glomerular basement membrane), variant pathogenicity was assessed using currently-accepted criteria, and variants were examined for gene location, and age at renal failure onset. Results were compared using Fisher's exact test (DNA Stata). Altogether 754 new DNA variants were identified, an increase of 25%, predominantly in people of European background. Of the 1168 COL4A5 variants, 504 (43%) were missense mutations, 273 (23%) splicing variants, 73 (6%) nonsense mutations, 169 (14%) short deletions and 76 (7%) complex or large deletions. Only 135 of the 432 Gly residues in the collagenous sequence were substituted (31%), which means that fewer than 10% of all possible variants have been identified. Both missense and nonsense mutations in COL4A5 were not randomly distributed but more common at the 70 CpG sequences (p<10-41 and p<0.001 respectively). Gly>Ala substitutions were underrepresented in all three genes (p< 0.0001) probably because of an association with a milder phenotype. The average age at end-stage renal failure was the same for all mutations in COL4A5 (24.4 ±7.8 years), COL4A3 (23.3 ± 9.3) and COL4A4 (25.4 ± 10.3) (COL4A5 and COL4A3, p = 0.45; COL4A5 and COL4A4, p = 0.55; COL4A3 and COL4A4, p = 0.41). For COL4A5, renal failure occurred sooner with non-missense than missense variants (p<0.01). For the COL4A3 and COL4A4 genes, age at renal failure occurred sooner with two non-missense variants (p = 0.08, and p = 0.01 respectively). Thus DNA variant characteristics that predict age at renal failure appeared to be the same for all three Alport genes. Founder mutations (with the pathogenic variant in at least 5 apparently- unrelated individuals) were not necessarily associated with a milder phenotype. This study illustrates the benefits when routine diagnostic laboratories share and analyse their data.