METHOD: Targeted sequencing of fourteen genes panel was performed to identify the mutations in 29 OI patients with type I, III, IV and V disease. The mutations were determined using Ion Torrent Suite software version 5 and variant annotation was conducted using ANNOVAR. The identified mutations were confirmed using Sanger sequencing and in silico analysis was performed to evaluate the effects of the candidate mutations at protein level.
RESULTS: Majority of patients had mutations in collagen genes, 48% (n = 14) in COL1A1 and 14% (n = 4) in COL1A2. Type I OI was caused by quantitative mutations in COL1A1 whereas most of type III and IV were due to qualitative mutations in both of the collagen genes. Those with quantitative mutations had milder clinical severity compared to qualitative mutations in terms of dentinogenesis imperfecta (DI), bone deformity and the ability to walk with aid. Furthermore, a few patients (28%, n = 8) had mutations in IFITM5, BMP1, P3H1 and SERPINF1.
CONCLUSION: Majority of our OI patients have mutations in collagen genes, similar to other OI populations worldwide. Genotype-phenotype analysis revealed that qualitative mutations had more severe clinical characteristics compared to quantitative mutations. It is crucial to identify the causative mutations and the clinical severity of OI patients may be predicted based on the types of mutations.
METHODS: We prospectively characterized the clinical features and disease burden in a consecutively-recruited multi-ethnic Asian PSP cohort. Patients were extensively phenotyped using the Movement Disorder Society (MDS-PSP) clinical diagnostic criteria and the PSP-Clinical Deficits Scale (PSP-CDS). Caregiver burden was measured using the modified Zarit Burden Interview (ZBI). Investigations (neuroimaging and genetic tests) were reviewed.
RESULTS: There were 104 patients (64.4% male; 67.3% Chinese, 21.2% Indians, 9.6% Malays), consisting of 48.1% Richardson syndrome (PSP-RS), 37.5% parkinsonian phenotype (PSP-P), and 10.6% progressive gait freezing phenotype (PSP-PGF). Mean age at motor onset was 66.3 ± 7.7 years, with no significant differences between the PSP phenotypes. Interestingly, REM-sleep behaviour disorder (RBD) symptoms and visual hallucinations (considered rare in PSP) were reported in 23.5% and 22.8% of patients, respectively, and a family history of possible neurodegenerative or movement disorder in 20.4%. PSP-CDS scores were highest (worst) in PSP-RS; and correlated moderately with disease duration (rs = 0.45, P
METHOD: A literature review was carried out, power and other issues discussed, and planned studies assessed.
RESULTS: Most of the genomic DNA sequence differences between any two people are common (frequency >5%) single nucleotide polymorphisms (SNPs). Because of localized patterns of correlation (linkage disequilibrium), 500,000 to 1,000,000 of these SNPs can test the hypothesis that one or more common variants explain part of the genetic risk for a disease. GWAS technologies can also detect some of the copy number variants (deletions and duplications) in the genome. Systematic study of rare variants will require large-scale resequencing analyses. GWAS methods have detected a remarkable number of robust genetic associations for dozens of common diseases and traits, leading to new pathophysiological hypotheses, although only small proportions of genetic variance have been explained thus far and therapeutic applications will require substantial further effort. Study design issues, power, and limitations are discussed. For psychiatric disorders, there are initial significant findings for common SNPs and for rare copy number variants, and many other studies are in progress.
CONCLUSIONS: GWAS of large samples have detected associations of common SNPs and of rare copy number variants with psychiatric disorders. More findings are likely, since larger GWAS samples detect larger numbers of common susceptibility variants, with smaller effects. The Psychiatric GWAS Consortium is conducting GWAS meta-analyses for schizophrenia, bipolar disorder, major depressive disorder, autism, and attention deficit hyperactivity disorder. Based on results for other diseases, larger samples will be required. The contribution of GWAS will depend on the true genetic architecture of each disorder.