VisCap: inference and visualization of germ-line copy-number variants from targeted clinical sequencing data.

PURPOSE: To develop and validate VisCap, a software program targeted to clinical laboratories for inference and visualization of germ-line copy-number variants (CNVs) from targeted next-generation sequencing data. METHODS: VisCap calculates the fraction of overall sequence coverage assigned to genomic intervals and computes log2 ratios of these values to the median of reference samples profiled using the same test configuration. Candidate CNVs are called when log2 ratios exceed user-defined thresholds. RESULTS: We optimized VisCap using 14 cases with known CNVs, followed by prospective analysis of 1,104 cases referred for diagnostic DNA sequencing. To verify calls in the prospective cohort, we used droplet digital polymerase chain reaction (PCR) to confirm 10/27 candidate CNVs and 72/72 copy-neutral genomic regions scored by VisCap. We also used a genome-wide bead array to confirm the absence of CNV calls across panels applied to 10 cases. To improve specificity, we instituted a visual scoring system that enabled experienced reviewers to differentiate true-positive from false-positive calls with minimal impact on laboratory workflow. CONCLUSIONS: VisCap is a sensitive method for inferring CNVs from targeted sequence data from targeted gene panels. Visual scoring of data underlying CNV calls is a critical step to reduce false-positive calls for follow-up testing.Genet Med 18 7, 712-719.Genetics in Medicine (2016); 18 7, 712-719. doi:10.1038/gim.2015.156.

The landscape of genetic variation in dilated cardiomyopathy as surveyed by clinical DNA sequencing.

PURPOSE: Dilated cardiomyopathy is characterized by substantial locus, allelic, and clinical heterogeneity that necessitates testing of many genes across clinically overlapping diseases. Few studies have sequenced sufficient individuals; thus, the contributions of individual genes and the pathogenic variant spectrum are still poorly defined. We analyzed 766 dilated cardiomyopathy patients tested over 5 years in our molecular diagnostics laboratory. METHODS: Patients were tested using gene panels of increasing size from 5 to 46 genes, including 121 cases tested with a multiple-cardiomyopathy next-generation panel covering 46 genes. All variants were reassessed using our current clinical-grade scoring system to eliminate false-positive disease associations that afflict many older analyses. RESULTS: Up to 37% of dilated cardiomyopathy cases carry a clinically relevant variant in one of 20 genes, titin (TTN) being the largest contributor (up to 14%). Desmoplakin (DSP), an arrhythmogenic right ventricular cardiomyopathy gene, contributed 2.4%, illustrating the utility of multidisease testing. The clinical sensitivity increased from 10 to 37% as gene panel sizes increased. However, the number of inconclusive cases also increased from 4.6 to 51%. CONCLUSION: Our data illustrate the utility of broad gene panels for genetically and clinically heterogeneous diseases but also highlight challenges as molecular diagnostics moves toward genome-wide testing.

Cognitive Decline in Alzheimer's Disease: Limited Clinical Utility for GWAS or Polygenic Risk Scores in a Clinical Trial Setting.

Introduction: Alzheimer's disease (AD) is a progressive and irreversible neurological disease. The genetics and molecular mechanisms underpinning differential cognitive decline in AD are not well understood; the genetics of AD risk have been studied far more assiduously. Materials and Methods: Two phase III clinical trials measuring cognitive decline over 48 weeks using Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog, n = 2060) and Clinical Dementia Rating-Sum of Boxes (CDR-SB, n = 1996) were retrospectively genotyped. A Genome-Wide Association Study (GWAS) was performed to identify and replicate genetic variants associated with cognitive decline. The relationship between polygenic risk score (PRS) and cognitive decline was tested to investigate the predictive power of aggregating many variants of individually small effect. Results: No loci met candidate gene or genome-wide significance. PRS explained a very small percentage of variance in rates of cognitive decline (ADAS-cog: 0.54%). Conclusions: These results suggest that incorporating genetic information in the prediction of cognitive decline in AD currently appears to have limited utility in clinical trials, consistent with small effect sizes estimated elsewhere. If AD progression is more heritable soon after disease onset, genetics may have more clinical utility.

Regression based predictor for p53 transactivation.

BACKGROUND: The p53 protein is a master regulator that controls the transcription of many genes in various pathways in response to a variety of stress signals. The extent of this regulation depends in part on the binding affinity of p53 to its response elements (REs). Traditional profile scores for p53 based on position weight matrices (PWM) are only a weak indicator of binding affinity because the level of binding also depends on various other factors such as interaction between the nucleotides and, in case of p53-REs, the extent of the spacer between the dimers. RESULTS: In the current study we introduce a novel in-silico predictor for p53-RE transactivation capability based on a combination of multidimensional scaling and multinomial logistic regression. Experimentally validated known p53-REs along with their transactivation capabilities are used for training. Through cross-validation studies we show that our method outperforms other existing methods. To demonstrate the utility of this method we (a) rank putative p53-REs of target genes and target microRNAs based on the predicted transactivation capability and (b) study the implication of polymorphisms overlapping p53-RE on its transactivation capability. CONCLUSION: Taking into account both nucleotide interactions and the spacer length of p53-RE, we have created a novel in-silico regression-based transactivation capability predictor for p53-REs and used it to analyze validated and novel p53-REs and to predict the impact of SNPs overlapping these elements.

PolyDoms: a whole genome database for the identification of non-synonymous coding SNPs with the potential to impact disease.

As knowledge of human genetic polymorphisms grows, so does the opportunity and challenge of identifying those polymorphisms that may impact the health or disease risk of an individual person. A critical need is to organize large-scale polymorphism analyses and to prioritize candidate non-synonymous coding SNPs (nsSNPs) that should be tested in experimental and epidemiological studies to establish their context-specific impacts on protein function. In addition, with emerging high-resolution clinical genetics testing, new polymorphisms must be analyzed in the context of all available protein feature knowledge including other known mutations and polymorphisms. To approach this, we developed PolyDoms (http://polydoms.cchmc.org/) as a database to integrate the results of multiple algorithmic procedures and functional criteria applied to the entire Entrez dbSNP dataset. In addition to predicting structural and functional impacts of all nsSNPs, filtering functions enable group-based identification of potentially harmful nsSNPs among multiple genes associated with specific diseases, anatomies, mammalian phenotypes, gene ontologies, pathways or protein domains. PolyDoms, thus, provides a means to derive a list of candidate SNPs to be evaluated in experimental or epidemiological studies for impact on protein functions and disease risk associations. PolyDoms will continue to be curated to improve its usefulness.

GenomeTrafac: a whole genome resource for the detection of transcription factor binding site clusters associated with conventional and microRNA encoding genes conserved between mouse and human gene orthologs.

Transcriptional cis-regulatory control regions frequently are found within non-coding DNA segments conserved across multi-species gene orthologs. Adopting a systematic gene-centric pipeline approach, we report here the development of a web-accessible database resource--GenomeTraFac (http://genometrafac.cchmc.org)--that allows genome-wide detection and characterization of compositionally similar cis-clusters that occur in gene orthologs between any two genomes for both microRNA genes as well as conventional RNA-encoding genes. Each ortholog gene pair can be scanned to visualize overall conserved sequence regions, and within these, the relative density of conserved cis-element motif clusters form graph peak structures. The results of these analyses can be mined en masse to identify most frequently represented cis-motifs in a list of genes. The system also provides a method for rapid evaluation and visualization of gene model-consistency between orthologs, and facilitates consideration of the potential impact of sequence variation in conserved non-coding regions to impact complex cis-element structures. Using the mouse and human genomes via the NCBI Reference Sequence database and the Sanger Institute miRBase, the system demonstrated the ability to identify validated transcription factor targets within promoter and distal genomic regulatory regions of both conventional and microRNA genes.

Mast cell transcriptional networks.

Unregulated activation of mast cells can contribute to the pathogenesis of inflammatory and allergic diseases, including asthma, rheumatoid arthritis, inflammatory bowel disease, and multiple sclerosis. Absence of mast cells in animal models can lead to impairment in the innate immune response to parasites and bacterial infections. Aberrant clonal accumulation and proliferation of mast cells can result in a variety of diseases ranging from benign cutaneous mastocytosis to systemic mastocytosis or mast cell leukemia. Understanding mast cell differentiation provides important insights into mechanisms of lineage selection during hematopoiesis and can provide targets for new drug development to treat mast cell disorders. In this review, we discuss controversies related to development, sites of origin, and the transcriptional program of mast cells.

CisMols Analyzer: identification of compositionally similar cis-element clusters in ortholog conserved regions of coordinately expressed genes.

Combinatorial interactions of sequence-specific trans-acting factors with localized genomic cis-element clusters are the principal mechanism for regulating tissue-specific and developmental gene expression. With the emergence of expanding numbers of genome-wide expression analyses, the identification of the cis-elements responsible for specific patterns of transcriptional regulation represents a critical area of investigation. Computational methods for the identification of functional cis-regulatory modules are difficult to devise, principally because of the short length and degenerate nature of individual cis-element binding sites and the inherent complexity that is generated by combinatorial interactions within cis-clusters. Filtering candidate cis-element clusters based on phylogenetic conservation is helpful for an individual ortholog gene pair, but combining data from cis-conservation and coordinate expression across multiple genes is a more difficult problem. To approach this, we have extended an ortholog gene-pair database with additional analytical architecture to allow for the analysis and identification of maximal numbers of compositionally similar and phylogenetically conserved cis-regulatory element clusters from a list of user-selected genes. The system has been successfully tested with a series of functionally related and microarray profile-based co-expressed ortholog pairs of promoters and genes using known regulatory regions as training sets and co-expressed genes in the olfactory and immunohematologic systems as test sets. CisMols Analyzer is accessible via a Web interface at http://cismols.cchmc.org/.

Designing algorithms for determining significance of DNA missense changes.

Humans differ from each other in their genomes by <1 %. This determines the difference in susceptibility to disease, phenotypes, and traits. Predominantly, when looking for causal disease mutations, protein-coding sequences are screened first since those have the highest probability of affecting the function of a protein. Recent technological advances have seen a rise in the number of experiments being conducted to study a variety of diseases from monogenic to complex traits. Several computational approaches have been developed to extract putative functional missense variants. In this chapter we review some of these approaches and describe a standard step-by-step procedure that can be used to classify variants for the purpose of clinical care. We also provide two examples demonstrating this approach, one for a patient with a dilated cardiomyopathy diagnosis, and the other for a patient with an unknown etiology undergoing whole-genome sequencing (WGS).

Comprehensive diagnostic testing for stereocilin: an approach for analyzing medically important genes with high homology.

Next-generation sequencing (NGS) technologies have revolutionized genetic testing by enabling simultaneous analysis of unprecedented numbers of genes. However, genes with high-sequence homology pose challenges to current NGS technologies. Because diagnostic sequencing is moving toward exome analysis, knowledge of these homologous genes is essential to avoid false positive and negative results. An example is the STRC gene, one of >70 genes known to contribute to the genetic basis of hearing loss. STRC is 99.6% identical to a pseudogene (pSTRC) and therefore inaccessible to standard NGS methodologies. The STRC locus is also known to be a common site for large deletions. Comprehensive diagnostic testing for inherited hearing loss therefore necessitates a combination of several approaches to avoid pseudogene interference. We have developed a clinical test that combines standard NGS and NGS-based copy number assessment supplemented with a long-range PCR-based Sanger or MiSeq assay to eliminate pseudogene contamination. By using this combination of assays we could identify biallelic STRC variants in 14% (95% CI, 8%-24%) of individuals with isolated nonsyndromic hearing loss who had previously tested negative on our 70-gene hearing loss panel, corresponding to a detection rate of 11.2% (95% CI, 6%-19%) for previously untested patients. This approach has broad applicability because medically significant genes for many disease areas include genes with high-sequence homology.

Rare variants in CFI, C3 and C9 are associated with high risk of advanced age-related macular degeneration.

To define the role of rare variants in advanced age-related macular degeneration (AMD) risk, we sequenced the exons of 681 genes within all reported AMD loci and related pathways in 2,493 cases and controls. We first tested each gene for increased or decreased burden of rare variants in cases compared to controls. We found that 7.8% of AMD cases compared to 2.3% of controls are carriers of rare missense CFI variants (odds ratio (OR) = 3.6; P = 2 × 10(-8)). There was a predominance of dysfunctional variants in cases compared to controls. We then tested individual variants for association with disease. We observed significant association with rare missense alleles in genes other than CFI. Genotyping in 5,115 independent samples confirmed associations with AMD of an allele in C3 encoding p.Lys155Gln (replication P = 3.5 × 10(-5), OR = 2.8; joint P = 5.2 × 10(-9), OR = 3.8) and an allele in C9 encoding p.Pro167Ser (replication P = 2.4 × 10(-5), OR = 2.2; joint P = 6.5 × 10(-7), OR = 2.2). Finally, we show that the allele of C3 encoding Gln155 results in resistance to proteolytic inactivation by CFH and CFI. These results implicate loss of C3 protein regulation and excessive alternative complement activation in AMD pathogenesis, thus informing both the direction of effect and mechanistic underpinnings of this disorder.

A rare penetrant mutation in CFH confers high risk of age-related macular degeneration.

Two common variants in the gene encoding complement factor H (CFH), the Y402H substitution (rs1061170, c.1204C>T)(1-4) and the intronic rs1410996 SNP(5,6), explain 17% of age-related macular degeneration (AMD) liability. However, proof for the involvement of CFH, as opposed to a neighboring transcript, and knowledge of the potential mechanism of susceptibility alleles are lacking. Assuming that rare functional variants might provide mechanistic insights, we used genotype data and high-throughput sequencing to discover a rare, high-risk CFH haplotype with a c.3628C>T mutation that resulted in an R1210C substitution. This allele has been implicated previously in atypical hemolytic uremic syndrome, and it abrogates C-terminal ligand binding(7,8). Genotyping R1210C in 2,423 AMD cases and 1,122 controls demonstrated high penetrance (present in 40 cases versus 1 control, P = 7.0 × 10(-6)) and an association with a 6-year-earlier onset of disease (P = 2.3 × 10(-6)). This result suggests that loss-of-function alleles at CFH are likely to drive AMD risk. This finding represents one of the first instances in which a common complex disease variant has led to the discovery of a rare penetrant mutation.

Evaluation of second-generation sequencing of 19 dilated cardiomyopathy genes for clinical applications.

Medical sequencing for diseases with locus and allelic heterogeneities has been limited by the high cost and low throughput of traditional sequencing technologies. "Second-generation" sequencing (SGS) technologies allow the parallel processing of a large number of genes and, therefore, offer great promise for medical sequencing; however, their use in clinical laboratories is still in its infancy. Our laboratory offers clinical resequencing for dilated cardiomyopathy (DCM) using an array-based platform that interrogates 19 of more than 30 genes known to cause DCM. We explored both the feasibility and cost effectiveness of using PCR amplification followed by SGS technology for sequencing these 19 genes in a set of five samples enriched for known sequence alterations (109 unique substitutions and 27 insertions and deletions). While the analytical sensitivity for substitutions was comparable to that of the DCM array (98%), SGS technology performed better than the DCM array for insertions and deletions (90.6% versus 58%). Overall, SGS performed substantially better than did the current array-based testing platform; however, the operational cost and projected turnaround time do not meet our current standards. Therefore, efficient capture methods and/or sample pooling strategies that shorten the turnaround time and decrease reagent and labor costs are needed before implementing this platform into routine clinical applications.

Pattern of sequence variation across 213 environmental response genes.

To promote the clinical and epidemiological studies that improve our understanding of human genetic susceptibility to environmental exposure, the Environmental Genome Project (EGP) has scanned 213 environmental response genes involved in DNA repair, cell cycle regulation, apoptosis, and metabolism for single nucleotide polymorphisms (SNPs). Many of these genes have been implicated by loss-of-function mutations associated with severe diseases attributable to decreased protection of genomic integrity. Therefore, the hypothesis for these studies is that individuals with functionally significant polymorphisms within these genes may be particularly susceptible to genotoxic environmental agents. On average, 20.4 kb of baseline genomic sequence or 86% of each gene, including a substantial amount of introns, all exons, and 1.3 kb upstream and downstream, were scanned for variations in the 90 samples of the Polymorphism Discovery Resource panel. The average nucleotide diversity across the 4.2 MB of these 213 genes is 6.7 x 10(-4), or one SNP every 1500 bp, when two random chromosomes are compared. The average candidate environmental response gene contains 26 PHASE inferred haplotypes, 34 common SNPs, 6.2 coding SNPs (cSNPs), and 2.5 nonsynonymous cSNPs. SIFT and Polyphen analysis of 541 nonsynonymous cSNPs identified 57 potentially deleterious SNPs. An additional eight polymorphisms predict altered protein translation. Because these genes represent 1% of all known human genes, extrapolation from these data predicts the total genomic set of cSNPs, nonsynonymous cSNPs, and potentially deleterious nonsynonymous cSNPs. The implications for the use of these data in direct and indirect association studies of environmentally induced diseases are discussed.