Genetic Sharing with Cardiovascular Disease Risk Factors and Diabetes Reveals Novel Bone Mineral Density Loci.

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity

Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC.

We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data

Use of whole-exome sequencing for diagnosis of Limb-Girdle muscular dystrophy

Importance To our knowledge, the efficacy of transferring next-generation sequencing from a research setting to neuromuscular clinics has never been evaluated. Objective To translate whole-exome sequencing (WES) to clinical practice for the genetic diagnosis of a large cohort of patients with limb-girdle muscular dystrophy (LGMD) for whom protein-based analyses and targeted Sanger sequencing failed to identify the genetic cause of their disorder. Design, Setting, and Participants We performed WES on 60 families with LGMDs (100 exomes). Data analysis was performed between January 6 and December 19, 2014, using the xBrowse bioinformatics interface (Broad Institute). Patients with LGMD were ascertained retrospectively through the Institute for Neuroscience and Muscle Research Biospecimen Bank between 2006 and 2014. Enrolled patients had been extensively investigated via protein studies and candidate gene sequencing and remained undiagnosed. Patients presented with more than 2 years of muscle weakness and with dystrophic or myopathic changes present in muscle biopsy specimens. Main Outcomes and Measures The diagnostic rate of LGMD in Australia and the relative frequencies of the different LGMD subtypes. Our central goals were to improve the genetic diagnosis of LGMD, investigate whether the WES platform provides adequate coverage of known LGMD-related genes, and identify new LGMD-related genes. Results With WES, we identified likely pathogenic mutations in known myopathy genes for 27 of 60 families. Twelve families had mutations in known LGMD-related genes. However, 15 families had variants in disease-related genes not typically associated with LGMD, highlighting the clinical overlap between LGMD and other myopathies. Common causes of phenotypic overlap were due to mutations in congenital muscular dystrophy–related genes (4 families) and collagen myopathy–related genes (4 families). Less common myopathies included metabolic myopathy (2 families), congenital myasthenic syndrome (DOK7), congenital myopathy (ACTA1), tubular aggregate myopathy (STIM1), myofibrillar myopathy (FLNC), and mutation of CHD7, usually associated with the CHARGE syndrome. Inclusion of family members increased the diagnostic efficacy of WES, with a diagnostic rate of 60% for “trios” (an affected proband with both parents) vs 40% for single probands. A follow-up screening of patients whose conditions were undiagnosed on a targeted neuromuscular disease–related gene panel did not improve our diagnostic yield. Conclusions and Relevance With WES, we achieved a diagnostic success rate of 45.0% in our difficult-to-diagnose cohort of patients with LGMD. We expand the clinical phenotypes associated with known myopathy genes, and we stress the importance of accurate clinical examination and histopathological results for interpretation of WES, with many diagnoses requiring follow-up review and ancillary investigations of biopsy specimens or serum samples