Development and Validation of Clinical Whole-Exome and Whole-Genome Sequencing for Detection of Germline Variants in Inherited Disease

Context.-With the decrease in the cost of sequencing, the clinical testing paradigm has shifted from single gene to gene panel and now whole-exome and whole-genome sequencing. Clinical laboratories are rapidly implementing next-generation sequencing-based whole-exome and whole-genome sequencing. Because a large number of targets are covered by whole-exome and whole-genome sequencing, it is critical that a laboratory perform appropriate validation studies, develop a quality assurance and quality control program, and participate in proficiency testing. Objective.-To provide recommendations for wholeexome and whole-genome sequencing assay design, validation, and implementation for the detection of germline variants associated in inherited disorders. Data Sources.-An example of trio sequencing, filtration and annotation of variants, and phenotypic consideration to arrive at clinical diagnosis is discussed. Conclusions.-It is critical that clinical laboratories planning to implement whole-exome and whole-genome sequencing design and validate the assay to specifications and ensure adequate performance prior to implementation. Test design specifications, including variant filtering and annotation, phenotypic consideration, guidance on consenting options, and reporting of incidental findings, are provided. These are important steps a laboratory must take to validate and implement whole-exome and whole-genome sequencing in a clinical setting for germline variants in inherited disorders

Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks

Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC), is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting “disease map” network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks

Resources and tools for rare disease variant interpretation

: Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world’s population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

Sequenciamento de nova geração : explorando aplicações clínicas de dados de Targeted Gene Panel e Whole Exome Sequencing

A tecnologia de sequenciamento de nova geração (next-generation sequencing – NGS) e suas aplicações tem sido cada vez mais utilizada na prática médica para elucidar a base molecular de doenças Mendelianas. Embora seja uma poderosa ferramenta de pesquisa, ainda existe uma importante transição quanto à análise dos dados entre as tecnologias tradicionais de sequenciamento e o NGS. A primeira parte deste trabalho aborda aspectos analíticos envolvidos nesta mudança, com foco na plataforma Ion Torrent Personal Genome Machine. Esta é uma plataforma amplamente utilizada para sequenciar painéis de genes, já que esta aplicação requer menor rendimento de dados. Este trabalho demonstra indicadores adequados para avaliar a qualidade de corridas de sequenciamento e também uma estratégia baseada em valores de profundidade de cobertura para avaliar a performance de amplicons em diferentes cenários. Por outro lado, o NGS permitiu a realização de estudos populacionais em larga escala que estão mudando nossa compreensão sobre as variações genéticas humanas. Um desses exemplos são as mutações até então chamadas de silenciosas, que estão sendo implicadas como causadoras de doenças humanas. A segunda parte deste trabalho investiga a patogenicidade de polimorfismos de núcleotídeo único sinônimos (synonymous single nucleotide polymorphisms – sSNP) baseado em dados públicos obtidos do Exome Aggregation Consortium (ExAC) (exac.broadinstitute.org/) utilizando o software Silent Variant Analysis (SilVA) (compbio.cs.toronto.edu/silva/) e outros recursos para reunir informações adicionais sobre consequências funcionais visando fornecer um panorama dos efeitos patogênicos de sSNP em mais de 60.000 exomas humanos. Nós demonstramos que de 1,691,045 variantes sinônimas, um total de 26,034 foram classificadas como patogênicas pelo SilVA, com frequência alélica menor que 0,05. Análises funcionais in silico revelaram que as variantes sinônimas patogênicas estão envolvidas em processos biológicos importantes, como regulação celular, metabolismo e transporte. Ao expor um cenário de variações sinônimas patogênicas em exomas humanos, nós concluímos que filtrar sSNP em workflows de priorização é razoável, no entanto em situações específicas os sSNP podem ser considerados. Pesquisas futuras neste campo poderão fornecer uma imagem clara do papel de tais variações em doenças genéticas.Next-generation sequencing (NGS) technologies and its applications are increasingly used in medicine to elucidate the molecular basis of Mendelian diseases. Although it is a powerful research tool, there is still an important transition regarding data analysis between traditional sequencing techniques and NGS. The first part of this work addresses analytical aspects involved on this switch-over, focusing on the Ion Torrent Personal Genome Machine platform. This is a widely used platform for sequencing gene panels, as this application demands lower throughput of data. We present indicators suitable to evaluate quality of sequencing runs and also a strategy based on depth of coverage values to evaluate amplicon performance on different scenarios. On the other hand, NGS enabled large-scale population studies that are changing our understanding about human genetic variations. One of these examples are the so-called silent mutations, that are being implied as causative of human diseases. The second part of this work investigates the pathogenicity of synonymous single nucleotide polymorphisms (sSNP) based on public data obtained from the Exome Aggregation Consortium (ExAC) (exac.broadinstitute.org/) using the software Silent Variant Analysis (SilVA) (compbio.cs.toronto.edu/silva/) and other sources to gather additional information about affected protein domains, mRNA folding and functional consequences aiming to provide a landscape of harmfulness of sSNP on more than 60,000 human exomes. We show that from 1,691,045 synonymous variants a total of 26,034 were classified as pathogenic and by SilVA, with allele frequency lower than 0.05. In silico functional analysis revealed that pathogenic synonymous variants found are involved in important biological process, such as cellular regulation, metabolism and transport. By exposing a scenario of pathogenic synonymous variants on human exomes we conclude that filtering out sSNP on prioritization workflows is reasonable, although in some specific cases sSNP should be considered. Future research on this field will provide a clear picture of such variations on genetic diseases

Disease Gene Characterization through Large-Scale Co-Expression Analysis

In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis