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Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Because individuals develop dementia as a manifestation of neurodegenerative or neurovascular disorder, there is a need to develop reliable approaches to their identification. We are undertaking an observational study (Ontario Neurodegenerative Disease Research Initiative [ONDRI]) that includes genomics, neuroimaging, and assessments of cognition as well as language, speech, gait, retinal imaging, and eye tracking. Disorders studied include Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, Parkinson's disease, and vascular cognitive impairment. Data from ONDRI will be collected into the Brain-CODE database to facilitate correlative analysis. ONDRI will provide a repertoire of endophenotyped individuals that will be a unique, publicly available resource.

The Ontario Neurodegenerative Disease Research Initiative (ONDRI) is a multimodal, multi-year, prospective observational cohort study to characterise five diseases: (1) Alzheimer's disease (AD) or amnestic single or multidomain mild cognitive impairment (aMCI) (AD/MCI); (2) amyotrophic lateral sclerosis (ALS); (3) frontotemporal dementia (FTD); (4) Parkinson's disease (PD); and (5) vascular cognitive impairment (VCI). The ONDRI Genomics subgroup is investigating the genetic basis of neurodegeneration. We have developed a custom next-generation-sequencing-based panel, ONDRISeq that targets 80 genes known to be associated with neurodegeneration. We processed DNA collected from 216 individuals diagnosed with one of the five diseases, on ONDRISeq. All runs were executed on a MiSeq instrument and subjected to rigorous quality control assessments. We also independently validated a subset of the variant calls using NeuroX (a genome-wide array for neurodegenerative disorders), TaqMan allelic discrimination assay, or Sanger sequencing. ONDRISeq consistently generated high-quality genotyping calls and on average, 92% of targeted bases are covered by at least 30 reads. We also observed 100% concordance for the variants identified via ONDRISeq and validated by other genomic technologies. We were successful in detecting known as well as novel rare variants in 72.2% of cases although not all variants are disease-causing. Using ONDRISeq, we also found that the APOE E4 allele had a frequency of 0.167 in these samples. Our optimised workflow highlights next-generation sequencing as a robust tool in elucidating the genetic basis of neurodegenerative diseases by screening multiple candidate genes simultaneously., Competing Interests: The authors declare no conflict of interest.

Background: Familial lipodystrophies are rare inherited disorders associated with redistribution of body fat and development of dyslipidemia, insulin resistance, and diabetes. We previously reported 2 siblings with unusual late-onset familial partial lipodystrophy in whom heretofore known causative genes had been excluded. We hypothesized they had a mutation in a novel lipodystrophy gene., Methods: Our approach centred on whole exome sequencing of the patients' DNA, together with genetic linkage analysis and a bioinformatic prioritization analysis. All candidate variants were assessed in silico and available family members were genotyped to assess segregation of mutations., Results: Our prioritization algorithm led us to a novel homozygous nonsense variant, namely p.Ala507fsTer563 in the hormone sensitive lipase gene encoding, an enzyme that is differentially expressed in adipocytes and steroidogenic tissues. Pathogenicity of the mutation was supported in bioinformatic analyses and variant cosegregation within the family., Conclusions: We have identified a novel nonsense mutation in hormone sensitive lipase gene, which likely explains the lipodystrophy phenotype observed in these patients., (Copyright © 2014 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)

Iron-sulfur (Fe-S) clusters are a class of highly conserved and ubiquitous prosthetic groups with unique chemical properties that allow the proteins that contain them, Fe-S proteins, to assist in various key biochemical pathways. Mutations in Fe-S proteins often disrupt Fe-S cluster assembly leading to a spectrum of severe disorders such as Friedreich's ataxia or iron-sulfur cluster assembly enzyme (ISCU) myopathy. Herein, we describe infantile mitochondrial complex II/III deficiency, a novel autosomal recessive mitochondrial disease characterized by lactic acidemia, hypotonia, respiratory chain complex II and III deficiency, multisystem organ failure and abnormal mitochondria. Through autozygosity mapping, exome sequencing, in silico analyses, population studies and functional tests, we identified c.215G>A, p.Arg72Gln in NFS1 as the likely causative mutation. We describe the first disease in man likely caused by deficiency in NFS1, a cysteine desulfurase that is implicated in respiratory chain function and iron maintenance by initiating Fe-S cluster biosynthesis. Our results further demonstrate the importance of sufficient NFS1 expression in human physiology.

Monogenic diseases have a distinctive familial inheritance that follows Mendel's laws, showing patterns like dominant, recessive, or X-linked. There are > 7000 monogenic diseases curated in databases, and together they account for up to 10% of all illnesses encountered in the emergency room or clinic. Despite the rarity of individual monogenic conditions, mapping their causative genes and mutations is important for several reasons. First, knowing the causative gene and mutation could provide actionable information for genetic counselling. Sometimes, knowing the gene and mutation allows for early diagnosis in affected families, which is important if there is an evidence-based intervention. Second, the implication of a mutant gene as being causative for a clinical phenotype provides strong evidence of the importance of the gene product in a cellular or biochemical pathway. Discovery of new molecular pathways in families with rare diseases can serve as the first step toward developing rational therapies to help not only affected families, but also patients with less extreme, nongenetic forms of the same condition. For instance, the study of rare patients with familial hypercholesterolemia helped in developing statin drugs, initially as a treatment for familial hypercholesterolemia but now a widely used therapy to reduce low-density lipoprotein cholesterol and cardiovascular disease risk., (Copyright © 2013 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)