Evaluating Familial Essential Tremor with Novel Genetic Approaches: Is it a Genotyping or Phenotyping Issue?

Background: Essential tremor is a common movement disorder with a strong heritable component. Large families with inherited forms of essential tremor have undergone genetic analyses by different approaches. However, our knowledge of genetic variants unequivocally linked to essential tremor is remarkably limited. Several explanations have been put forth to explain this challenge, including the possibility of mutations in non-coding areas of the genome. Methods: We encountered a family with highly penetrant, autosomal dominant tremor. We hypothesized that, if a single coding gene mutation was responsible for the phenotype, novel genetic tools would allow us to identify it. We employed single nucleotide polymorphism (SNP) arrays in 17 members of this family followed by next generation whole-exome sequencing in five affected subjects. Results: We did not identify any copy number variant or mutation that segregated with the disease phenotype. Discussion: This study emphasizes the remarkably challenging field of tremor genetics and indicates that future studies should perhaps shift to analysis of the non-coding genome. Keywords: Essential tremor, genetics, dystonia Citation: Gonzalez-Alegre P, Di Paola J, Wang K, et al. Evaluating familial essential tremor with novel genetic approaches: Is it a genotyping or phenotyping issue? Tremor Other Hyperkinet Mov. 2014; 4. doi: 10.7916/D8FB51G

Expanding the genotypic and phenotypic spectrum of severe serine biosynthesis disorders.

Serine biosynthesis disorders comprise a spectrum of very rare autosomal recessive inborn errors of metabolism with wide phenotypic variability. Neu-Laxova syndrome represents the most severe expression and is characterized by multiple congenital anomalies and pre- or perinatal lethality. Here, we present the mutation spectrum and a detailed phenotypic analysis in 15 unrelated families with severe types of serine biosynthesis disorders. We identified likely disease-causing variants in the PHGDH and PSAT1 genes, several of which have not been reported previously. Phenotype analysis and a comprehensive review of the literature corroborates the evidence that serine biosynthesis disorders represent a continuum with varying degrees of phenotypic expression and suggest that even gradual differences at the severe end of the spectrum may be correlated with particular genotypes. We postulate that the individual residual enzyme activity of mutant proteins is the major determinant of the phenotypic variability, but further functional studies are needed to explore effects at the enzyme protein level.We are indebted to all families for participating in this study. We would like to acknowledge Dr. Natasha Laidlew, who initially suggested the diagnosis in one of the cases and provided important phenotypic information, and Dr. María-Luisa Martínez-Fernández for the critical management of biosamples in ECEMC Program of Spain. Financial assistance was received in support of the study by grants from the German Federal Ministry of Education and Research (BMBF) (GeNeRARe, FKZ: 01GM1519D) to M. Z. and from the Institute of Health Carlos III: Convenio ISCIII-ASEREMAC, and Fundación 1000 sobre Defectos Congénitos, of Spain to E. B.-S. and I. R. G.S

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Lactobacillus delbrueckii as the Cause of Urinary Tract Infection▿

Lactobacilli are part of the normal bacterial flora of the vagina and are typically considered contaminants when cultured from urine specimens of female patients. Here we describe the case of a female patient with chronic pyuria and urinary tract symptoms in which Lactobacillus delbrueckii was determined to be the causative microorganism

Additive and Interactive Genetically Contextual Effects of HbA1c on cg19693031 Methylation in Type 2 Diabetes

Type 2 diabetes mellitus (T2D) has a complex genetic and environmental architecture that underlies its development and clinical presentation. Despite the identification of well over a hundred genetic variants and CpG sites that associate with T2D, a robust biosignature that could be used to prevent or forestall clinical disease has not been developed. Based on the premise that underlying genetic variation influences DNA methylation (DNAm) independently of or in combination with environmental exposures, we assessed the ability of local and distal gene x methylation (GxMeth) interactive effects to improve cg19693031 models for predicting T2D status in an African American cohort. Using genome-wide genetic data from 506 subjects, we identified a total of 1476 GxMeth terms associated with HbA1c values. The GxMeth SNPs map to biological pathways associated with the development and complications of T2D, with genetically contextual differences in methylation observed only in diabetic subjects for two GxMeth SNPs (rs2390998 AG vs. GG, p = 4.63 × 10−11, Δβ = 13%, effect size = 0.16 [95% CI = 0.05, 0.32]; rs1074390 AA vs. GG, p = 3.93 × 10−4, Δβ = 9%, effect size = 0.38 [95% CI = 0.12, 0.56]. Using a repeated stratified k-fold cross-validation approach, a series of balanced random forest classifiers with random under-sampling were built to evaluate the addition of GxMeth terms to cg19693031 models to discriminate between normoglycemic controls versus T2D subjects. The results were compared to those obtained from models incorporating only the covariates (age, sex and BMI) and the addition of cg19693031. We found a post-pruned classifier incorporating 10 GxMeth SNPs and cg19693031 adjusted for covariates predicted the T2D status, with the AUC, sensitivity, specificity and precision of the positive target class being 0.76, 0.81, 0.70 and 0.63, respectively. Comparatively, the AUC, sensitivity, specificity and precision using the covariates and cg19693031 were only 0.71, 0.74, 0.67 and 0.59, respectively. Collectively, we demonstrate correcting for genetic confounding of cg19693031 improves its ability to detect type 2 diabetes. We conclude that an integrated genetic–epigenetic approach could inform personalized medicine programming for more effective prevention and treatment of T2D

Additive and Interactive Genetically Contextual Effects of HbA1c on cg19693031 Methylation in Type 2 Diabetes

Type 2 diabetes mellitus (T2D) has a complex genetic and environmental architecture that underlies its development and clinical presentation. Despite the identification of well over a hundred genetic variants and CpG sites that associate with T2D, a robust biosignature that could be used to prevent or forestall clinical disease has not been developed. Based on the premise that underlying genetic variation influences DNA methylation (DNAm) independently of or in combination with environmental exposures, we assessed the ability of local and distal gene x methylation (GxMeth) interactive effects to improve cg19693031 models for predicting T2D status in an African American cohort. Using genome-wide genetic data from 506 subjects, we identified a total of 1476 GxMeth terms associated with HbA1c values. The GxMeth SNPs map to biological pathways associated with the development and complications of T2D, with genetically contextual differences in methylation observed only in diabetic subjects for two GxMeth SNPs (rs2390998 AG vs. GG, p = 4.63 × 10−11, Δβ = 13%, effect size = 0.16 [95% CI = 0.05, 0.32]; rs1074390 AA vs. GG, p = 3.93 × 10−4, Δβ = 9%, effect size = 0.38 [95% CI = 0.12, 0.56]. Using a repeated stratified k-fold cross-validation approach, a series of balanced random forest classifiers with random under-sampling were built to evaluate the addition of GxMeth terms to cg19693031 models to discriminate between normoglycemic controls versus T2D subjects. The results were compared to those obtained from models incorporating only the covariates (age, sex and BMI) and the addition of cg19693031. We found a post-pruned classifier incorporating 10 GxMeth SNPs and cg19693031 adjusted for covariates predicted the T2D status, with the AUC, sensitivity, specificity and precision of the positive target class being 0.76, 0.81, 0.70 and 0.63, respectively. Comparatively, the AUC, sensitivity, specificity and precision using the covariates and cg19693031 were only 0.71, 0.74, 0.67 and 0.59, respectively. Collectively, we demonstrate correcting for genetic confounding of cg19693031 improves its ability to detect type 2 diabetes. We conclude that an integrated genetic–epigenetic approach could inform personalized medicine programming for more effective prevention and treatment of T2D

Autism Linked to Increased Oncogene Mutations but Decreased Cancer Rate.

Autism spectrum disorder (ASD) is one phenotypic aspect of many monogenic, hereditary cancer syndromes. Pleiotropic effects of cancer genes on the autism phenotype could lead to repurposing of oncology medications to treat this increasingly prevalent neurodevelopmental condition for which there is currently no treatment. To explore this hypothesis we sought to discover whether autistic patients more often have rare coding, single-nucleotide variants within tumor suppressor and oncogenes and whether autistic patients are more often diagnosed with neoplasms. Exome-sequencing data from the ARRA Autism Sequencing Collaboration was compared to that of a control cohort from the Exome Variant Server database revealing that rare, coding variants within oncogenes were enriched for in the ARRA ASD cohort (p<1.0 x 10(-8)). In contrast, variants were not significantly enriched in tumor suppressor genes. Phenotypically, children and adults with ASD exhibited a protective effect against cancer, with a frequency of 1.3% vs. 3.9% (p<0.001), but the protective effect decreased with age. The odds ratio of neoplasm for those with ASD relative to controls was 0.06 (95% CI: 0.02, 0.19; p<0.0001) in the 0 to 14 age group; 0.35 (95% CI: 0.14, 0.87; p = 0.024) in the 15 to 29 age group; 0.41 (95% CI: 0.15, 1.17; p = 0.095) in the 30 to 54 age group; and 0.49 (95% CI: 0.14, 1.74; p = 0.267) in those 55 and older. Both males and females demonstrated the protective effect. These findings suggest that defects in cellular proliferation, and potentially senescence, might influence both autism and neoplasm, and already approved drugs targeting oncogenic pathways might also have therapeutic value for treating autism