Network‐Informed Gene Ranking Tackles Genetic Heterogeneity in Exome‐Sequencing Studies of Monogenic Disease

Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole‐exome sequencing generates a large number of candidate disease‐causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary. To address this need, we present HetRank, a flexible gene‐ranking method that incorporates interaction network data. We first show that different genes underlying the same monogenic disease are frequently connected in protein interaction networks. This motivates the central premise of HetRank: those genes carrying potentially pathogenic variants and whose network neighbors do so in other affected individuals are strong candidates for follow‐up study. By simulating 1,000 exome sequencing studies (20,000 exomes in total), we model varying degrees of genetic heterogeneity and show that HetRank consistently prioritizes more disease‐causing genes than existing analysis methods. We also demonstrate a proof‐of‐principle application of the method to prioritize genes causing Adams‐Oliver syndrome, a genetically heterogeneous rare disease. An implementation of HetRank in R is available via the Website http://sourceforge.net/p/hetrank/ .     

wKGGSeq: A Comprehensive Strategy‐Based and Disease‐Targeted Online Framework to Facilitate Exome Sequencing Studies of Inherited Disorders

With the rapid advances in high‐throughput sequencing technologies, exome sequencing and targeted region sequencing have become routine approaches for identifying mutations of inherited disorders in both genetics research and molecular diagnosis. There is an imminent need for comprehensive and easy‐to‐use downstream analysis tools to isolate causal mutations in exome sequencing studies. We have developed a user‐friendly online framework, wKGGSeq, to provide systematic annotation, filtration, prioritization, and visualization functions for characterizing causal mutation(s) in exome sequencing studies of inherited disorders. wKGGSeq provides: (1) a novel strategy‐based procedure for downstream analysis of a large amount of exome sequencing data and (2) a disease‐targeted analysis procedure to facilitate clinical diagnosis of well‐studied genetic diseases. In addition, it is also equipped with abundant online annotation functions for sequence variants. We demonstrate that wKGGSeq either outperforms or is comparable to two popular tools in several real exome sequencing samples. This tool will greatly facilitate the downstream analysis of exome sequencing data and can play a useful role for researchers and clinicians in identifying causal mutations of inherited disorders. The wKGGSeq is freely available at http://statgenpro.psychiatry.hku.hk/wkggseq or http://jjwanglab.org/wkggseq , and will be updated frequently.     

eDiVA—Classification and prioritization of pathogenic variants for clinical diagnostics

Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole‐exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20–30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu ), an automated computational framework for identification of causal genetic variants (coding/splicing single‐nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent–child trios. eDiVA combines next‐generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning‐based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state‐of‐the‐art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.     

Genetic diagnosis of kidney disease by whole exome sequencing and its clinical application

The genetic spectrum of genetic kidney diseases (GKD) and the application of genetic diagnoses to patient care were assessed by whole exome sequencing (WES) of the DNA of 172 pediatric or adult patients with various kidney diseases. WES diagnosed genetic diseases in 63 (36.6%) patients. The diagnostic yields in patients with glomerulopathy were 33.8% (25/74 pts) due to variants in 10 genes, 58.8% (20/34) in patients with tubulointerstitial disease due to variants in 18 genes, 33.3% (15/45) in patients with cystic disease/ciliopathy due to variants in 10 genes, 18.2% (2/11) in patients with congenital anomalies of the kidneys and urinary tract (CAKUT) due to variants in two genes, and 12.5% (1/8) in patients with end stage kidney disease (ESKD). The diagnosis rate was high in patients aged <1–6 years (46–50.0%), and low in patients aged ≥40 years (9.1%). Renal phenotype was reclassified in 10 (15.9%) of 63 patients and clinical management altered in 10 (15.9%) of 63 patients after genetic diagnosis. In conclusion, these findings demonstrated the diagnostic utility of WES and its effective clinical application in patients, with various kinds of kidney diseases, across the different age groups.     

Robust identification of deletions in exome and genome sequence data based on clustering of Mendelian errors

Multiple tools have been developed to identify copy number variants (CNVs) from whole exome (WES) and whole genome sequencing (WGS) data. Current tools such as XHMM for WES and CNVnator for WGS identify CNVs based on changes in read depth. For WGS, other methods to identify CNVs include utilizing discordant read pairs and split reads and genome‐wide local assembly with tools such as Lumpy and SvABA, respectively. Here, we introduce a new method to identify deletion CNVs from WES and WGS trio data based on the clustering of Mendelian errors (MEs). Using our Mendelian Error Method (MEM), we identified 127 deletions (inherited and de novo) in 2,601 WES trios from the Pediatric Cardiac Genomics Consortium, with a validation rate of 88% by digital droplet PCR. MEM identified additional de novo deletions compared with XHMM, and a significant enrichment of 15q11.2 deletions compared with controls. In addition, MEM identified eight cases of uniparental disomy, sample switches, and DNA contamination. We applied MEM to WGS data from the Genome In A Bottle Ashkenazi trio and identified deletions with 97% specificity. MEM provides a robust, computationally inexpensive method for identifying deletions, and an orthogonal approach for verifying deletions called by other tools.     

New Tools for Mendelian Disease Gene Identification: PhenoDB Variant Analysis Module; and GeneMatcher,a Web‐Based Tool for Linking Investigators with an Interest in the Same Gene

Identifying the causative variant from among the thousands identified by whole‐exome sequencing or whole‐genome sequencing is a formidable challenge. To make this process as efficient and flexible as possible, we have developed a Variant Analysis Module coupled to our previously described Web‐based phenotype intake tool, PhenoDB ( http://researchphenodb.net and http://phenodb.org ). When a small number of candidate‐causative variants have been identified in a study of a particular patient or family, a second, more difficult challenge becomes proof of causality for any given variant. One approach to this problem is to find other cases with a similar phenotype and mutations in the same candidate gene. Alternatively, it may be possible to develop biological evidence for causality, an approach that is assisted by making connections to basic scientists studying the gene of interest, often in the setting of a model organism. Both of these strategies benefit from an open access, online site where individual clinicians and investigators could post genes of interest. To this end, we developed GeneMatcher ( http://genematcher.org ), a freely accessible Website that enables connections between clinicians and researchers across the world who share an interest in the same gene(s).     

Age‐related macular degeneration—clinical review and genetics update

Age‐related macular degeneration (AMD) is the leading cause of central vision impairment in persons over the age of 50 years in developed countries. Both genetic and non‐genetic (environmental) factors play major roles in AMD etiology, and multiple gene variants and lifestyle factors such as smoking have been associated with the disease. While dissecting the basic etiology of the disease remains a major challenge, current genetic knowledge has provided opportunities for improved risk assessment, molecular diagnosis and clinical testing of genetic variants in AMD treatment and management. This review addresses the potential of translating the wealth of genetic findings for improved risk prediction and therapeutic intervention in AMD patients. Finally, we discuss the recent advancement in genetics and genomics and the future prospective of personalized medicine in AMD patients.     

Genetics of Wilson disease and Wilson-like phenotype in a clinical series from eastern Spain

Wilson's disease (WD) is an autosomal recessive disorder caused by ATP7B mutations. Subjects with only one mutation may show clinical signs and individuals with biallelic changes may remain asymptomatic. We aimed to achieve a conclusive genetic diagnosis for 34 patients clinically diagnosed of WD. Genetic analysis comprised from analysis of exons to WES (whole exome sequencing), including promoter, introns, UTRs (untranslated regions), besides of study of large deletions/duplications by MLPA (multiplex ligation-dependent probe amplification). Biallelic ATP7B mutations were identified in 30 patients, so that four patients were analyzed using WES. Two affected siblings resulted to be compound heterozygous for mutations in CCDC115, which is involved in a form of congenital disorder of glycosylation. In sum, the majority of patients with a WD phenotype carry ATP7B mutations. However, if genetic diagnosis is not achieved, additional genes should be considered because other disorders may mimic WD.     

Meta‐analysis of genotype‐phenotype associations in Bardet‐Biedl syndrome uncovers differences among causative genes

Bardet‐Biedl syndrome (BBS) is a recessive genetic disease causing multiple organ anomalies. Most patients carry mutations in genes encoding for the subunits of the BBSome, an octameric ciliary transport complex, or accessory proteins involved in the BBSome assembly or function. BBS proteins have been extensively studied using in vitro, cellular, and animal models. However, the molecular functions of particular BBS proteins and the etiology of the BBS symptoms are still largely elusive. In this study, we applied a meta‐analysis approach to study the genotype‐phenotype association in humans using our database of all reported BBS patients. The analysis revealed that the identity of the causative gene and the character of the mutation partially predict the clinical outcome of the disease. Besides their potential use for clinical prognosis, our analysis revealed functional differences of particular BBS genes in humans. Core BBSome subunits BBS2, BBS7, and BBS9 manifest as more critical for the function and development of kidneys than peripheral subunits BBS1, BBS4, and BBS8/TTC8, suggesting that incomplete BBSome retains residual function at least in the kidney.     

The clinical utility of whole‐exome sequencing in the context of rare diseases – the changing tides of medical practice

Whole‐exome sequencing (WES) carries the potential to facilitate the identification of disease causing genes. This is particularly relevant concerning rare diseases, which proves particularly difficult for physicians to diagnose. However, the complexity of this technology renders its applicability onto the clinical setting uncertain. Our study thus aims to understand physicians' perspectives regarding the clinical utility of WES, particularly for providing a diagnosis for patients with rare diseases. Ten semi‐structured interviews were conducted with physicians with experience and familiarity with WES, and the major themes that emerged from our interviews were (i) the relevance of WES in diagnosing patients with rare diseases (appropriateness); (ii) the cost‐effectiveness of WES (accessibility), (iii) the practical issues related to the clinical implementation of WES (practicability); and (iv) ethical, legal and social issues (acceptability). Our study highlights how the clinical implementation of WES presents additional challenges where rare diseases are taken into consideration.     

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype‐weighted knowledge in the CAGI SickKids5 clinical genomes challenge

Precise identification of causative variants from whole‐genome sequencing data, including both coding and noncoding variants, is challenging. The Critical Assessment of Genome Interpretation 5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of the 24 whole‐genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state‐of‐the‐art pipeline. The patients have a range of eye, neurological, and connective‐tissue disorders. We used a gene‐centric approach to address this problem, assigning each gene a multiphenotype‐matching score. Mutations in the top‐scoring genes for each phenotype profile were ranked on a 6‐point scale of pathogenicity probability, resulting in an approximately equal number of top‐ranked coding and noncoding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the postsubmission phase, after careful screening of the genes in the correct genome, we identified additional potential diagnostic variants, a high proportion of which are noncoding.     

Determinants of depressive mood in coronary artery disease patients with obstructive sleep apnea and response to continuous positive airway pressure treatment in non‐sleepy and sleepy phenotypes in the RICCADSA cohort

We explored determinants of depressive mood in adults with coronary artery disease and obstructive sleep apnea and response to positive airway pressure treatment in sleepy and non‐sleepy phenotypes. In this secondary analysis of the RICCADSA trial conducted in Sweden, 493 cardiac patients with obstructive sleep apnea (n = 386) or no obstructive sleep apnea (n = 107) with complete Epworth Sleepiness Scale and Zung Self‐rating Depression Scale questionnaires were included. Sleepy (Epworth Sleepiness Scale ≥10) versus non‐sleepy (Epworth Sleepiness Scale <10) patients with depressive mood (Zung Self‐rating Depression Scale score ≥50) were evaluated after 3 and 12 months of positive airway pressure treatment. In all, 133 patients (27.0%) had depressive mood (29.3% of obstructive sleep apnea versus 18.7% of no obstructive sleep apnea; p = 0.029), with a higher percentage among the sleepy phenotype (36.9% versus 24.5%; p = 0.009). In multivariate analysis, depressive mood was significantly associated with female sex, body mass index and Epworth Sleepiness Scale. Among 97 obstructive sleep apnea patients with depressive mood at baseline, there was a significant reduction in the scores at follow‐up both in the sleepy and non‐sleepy patients allocated to positive airway pressure treatment, whereas no significant changes were observed in the untreated group (p = 0.033). The device use (hr/night) predicted improvement in mood (odds ratio, 1.33; 95% confidence interval, 1.10–1.61; p = 0.003) adjusted for age, female sex, body mass index, left ventricular ejection fraction, apnea–hypopnea index and delta Epworth Sleepiness Scale score. We conclude that obstructive sleep apnea was associated with depressive mood in adults with coronary artery disease. Treatment with positive airway pressure improved mood in both phenotypes, independent of the confounding factors.