Phenotypic information in genomic variant databases enhances clinical care and research: the International Standards for Cytogenomic Arrays Consortium experience

Aicardi Syndrome Is a Genetically Heterogeneous Disorder

Aicardi Syndrome (AIC) is a rare neurodevelopmental disorder recognized by the classical triad of agenesis of the corpus callosum, chorioretinal lacunae and infantile epileptic spasms syndrome. The diagnostic criteria of AIC were revised in 2005 to include additional phenotypes that are frequently observed in this patient group. AIC has been traditionally considered as X-linked and male lethal because it almost exclusively affects females. Despite numerous genetic and genomic investigations on AIC, a unifying X-linked cause has not been identified. Here, we performed exome and genome sequencing of 10 females with AIC or suspected AIC based on current criteria. We identified a unique de novo variant, each in different genes: KMT2B, SLF1, SMARCB1, SZT2 and WNT8B, in five of these females. Notably, genomic analyses of coding and non-coding single nucleotide variants, short tandem repeats and structural variation highlighted a distinct lack of X-linked candidate genes. We assessed the likely pathogenicity of our candidate autosomal variants using the TOPflash assay for WNT8B and morpholino knockdown in zebrafish (Danio rerio) embryos for other candidates. We show expression of Wnt8b and Slf1 are restricted to clinically relevant cortical tissues during mouse development. Our findings suggest that AIC is genetically heterogeneous with implicated genes converging on molecular pathways central to cortical development.

Diagnostic Value of Microarray Method in Autism Spectrum Disorder, Intellectual Disability, and Multiple Congenital Anomalies and Some Candidate Genes for Autism: Experience of Two Centers

Objective:

This study aimed to demonstrate the diagnostic value of microarray testing in autism spectrum disorder, intellectual disability, and multiple congenital anomalies of unknown etiology, as well as to report some potential candidate genes for autism.

Methods:

Microarray analysis records between January 2016 and December 2017 from two Genetic Diagnostic Centers in Turkey, Kanuni Sultan Suleyman and Adana Numune Training and Research Hospital, were compiled. Detected copy number variations (CNVs) were classified as benign, likely benign, variants of uncertain significance (VUS), likely pathogenic, and pathogenic according to American College of Medical Genetics and Genomics guidelines. The clinical findings of the some patients and the literature data were compared.

Results:

In 109 (24.5%) of 445 patients, a total of 163 CNVs with reporting criterion feature were detected. Sixty-nine (42%) and 8 (5%) of these were evaluated as pathogenic and likely pathogenic, respectively. Fifteen (9%) CNVs were also evaluated as VUS. Pathogenic or likely pathogenic CNVs were detected in 61 (13.6%) of 445 patients.

Conclusions:

We found that the probability of elucidating the etiology of microarray method in autism spectrum disorder, intellectual disability, and multiple congenital anomalies is 13.6% with a percentage similar to the literature. We suggest that the MYT1L, PXDN, TPO, and AUTS2 genes are all strong candidate genes for autism spectrum disorders. We detailed the clinical findings of the cases and reported that some CNV regions in the genome may be associated with autism.

Identifying patients and assessing variant pathogenicity for an autosomal dominant disease-driving gene

Identifying a disease gene and determining its causality in patients can be challenging. Here, we present an approach to predicting the pathogenicity of deletions and missense variants for an autosomal dominant gene. We provide online resources for identifying patients and determining constraint metrics to isolate the causal gene among several candidates encompassed in a shared region of deletion. We also provide instructions for optimizing functional annotation programs that may be otherwise inaccessible to a nonexpert or novice in computational approaches. For complete details on the use and execution of this protocol, please refer to Gennarino et al. (2018).

Whole Exome Sequencing in 16p13.11 Microdeletion Patients Reveals New Variants Through Deductive and Systems Medicine Approaches

The 16p13.11 microdeletion, whose prevalence in the general population is about 0.04%, is known in literature as a predisposition factor to neurodevelopmental disorders, being found in about 0.13% of patients with schizophrenia, in 0.5–0.6% of patient with epilepsy, cognitive impairment, autism spectrum disorder (ASD) and aggressiveness. The goal of this study was to identify a specific gene set pattern unique for the affected patients in comparison with other familial components. Due to the incomplete penetrance of this copy number variant (CNV), we studied by whole exome sequencing (WES), with particular regard of 850 SFARI genes, three families with an affected member carrier of inherited 16p13.11 and 16p13.11p12.3 microdeletion and one family with an affected member with a de novo 16p13.11 microdeletion. By combining a deductive approach together with personalized network models, we identified gene signatures potentially capable of explaining the clinical phenotype. Candidate variants in genes of interest were identified as possibly involved in determining the neurological phenotype of the four patients, such as compound heterozygosity in CECR2, variants in MTOR and RICTOR genes, compound heterozygous single nucleotide variants in the LRRK2 gene. Moreover, genes present in the microdeletion region were partially present as central nodes, with a focus on NDE1. No additional pathogenetic or uncertain CNVs were found in all four patients. No significant variants were detected in genes included in the microdeletion in patients 1, 2 and 3, excluding the finding of unmasked recessive variants. In conclusion, WES is a fundamental tool in the genetic investigation of patients having a predisposing variant, which is not sufficient to define the clinical phenotype. Moreover, the analysis of WES data using Systems medicine tools, such as personalized network models, led to the prioritization of genes on a high throughput scale and to discover variants in genes that were not prioritized at first.

A novel 1p33p32.2 deletion involving SCP2, ORC1, and DAB1 genes in a patient with craniofacial dysplasia, short stature, developmental delay, and leukoencephalopathy: A case report

INTRODUCTION

Microdeletion syndromes occur from deletion of 5Mb of a chromosome in approximately 5% of patients with unexplained intellectual disability. Interstitial microdeletions at bands 1p33 and 1p32.2 of the short arm of chromosome 1 are rare and have not been previously reported in relation to disease.

PATIENT CONCERNS

We present a case of a 39-month boy with Pierre Robin sequence, development delay/intellectual disability, growth retardation, short stature, leukoencephalopathy, craniofacial dysplasia, and speech delay. The child was referred to the Child health care department in October 2014 for his delayed language development and aggravated aggression.

DIAGNOSIS

Molecular diagnostic testing with G-band karyotyping was normal but clinical microarray analysis detected a 10 Mb microdeletion at 1p33p32.2.

INTERVENTIONS

The patient received rehabilitation.

OUTCOMES

Three candidate genes were pinpointed to the deleted area, including ORC1, SCP2, and DAB1. Phenotype-genotype analysis suggested that these three genes are likely to be responsible for the main phenotypes observed in the patient, such as microcephaly, growth retardation, short stature, leukoencephalopathy, and development delay/intellectual disability.

CONCLUSIONS

The spectrum of phenotypes this case presented with are likely to be caused by 1p33p32.2 deletion which could represent a new microdeletion syndrome.

ClinGen's GenomeConnect registry enables patient-centered data sharing

GenomeConnect, the NIH-funded Clinical Genome Resource (ClinGen) patient registry, engages patients in data sharing to support the goal of creating a genomic knowledge base to inform clinical care and research. Participant self-reported health information and genomic variants from genetic testing reports are curated and shared with public databases, such as ClinVar. There are four primary benefits of GenomeConnect: (1) sharing novel genomic data-47.9% of variants were new to ClinVar, highlighting patients as a genomic data source; (2) contributing additional phenotypic information-of the 52.1% of variants already in ClinVar, GenomeConnect provided enhanced case-level data; (3) providing a way for patients to receive variant classification updates if the reporting laboratory submits to ClinVar-97.3% of responding participants opted to receive such information and 13 updates have been identified; and (4) supporting connections with others, including other participants, clinicians, and researchers to enable the exchange of information and support-60.4% of participants have opted to partake in participant matching. Moving forward, ClinGen plans to increase patient-centric data sharing by partnering with other existing patient groups. By engaging patients, more information is contributed to the public knowledge base, benefiting both patients and the genomics community.

Characterization of the extra copy of TPOX locus with tri-allelic pattern

BACKGROUND

An STR locus with tri-allelic pattern is occasionally observed in routine forensic casework. The extra copy of TPOX locus with tri-allelic pattern in populations has been assumed to be inserted into an X chromosome, which took place forth before the Bantu expansion in Africa. Nonetheless, the exact location of the duplication and the form of rearrangement in the human genome has not been clarified yet.

RESULTS

In this study, we investigated the extra copy of type 2 tri-allelic pattern at TPOX in various populations. While allele 10 is the major third allele in Africa, allele 11 appears more frequent in America and overwhelming in Chinese and Korean populations, which might attribute to the population substructures. Results from the investigation of family cases showed that the transmission of the extra allele had a similar genetic pattern of autosomal genes. Furthermore, a whole-genome sequencing followed by bioinformatics analysis revealed that the intact form of chromosomal duplication and rearrangement occurred ~ 407 kb away from the authentic TPOX locus on chromosome 2 in two cases. The breakpoints of the insertion were further validated in most other tri-allelic subjects, which can imply the identical origin from the ancestral extra copy. Nevertheless, de novo chromosomal duplication and rearrangement at thyroid peroxidase gene occur in populations.

CONCLUSIONS

Instead of the extra allele 10 in African populations, the main third allele at TPOX with tri-allelic pattern is allele 11 in Chinese and Korean populations. The insertion of the extra copy into chromosome 2 occurs in most subjects with tri-allelic pattern at TPOX and demonstrates the transmission of the third allele from parents to offspring. The breakpoints of the ancestral extra copy are defined, which shows evidence of its inheritance from African populations. In addition, the simple validation method would help improve tri-allelic pattern calling, distinguish de novo chromosomal rearrangements, and also count the frequencies among different geographic regions. Therefore, the statistical interpretation of tri-allelic pattern at TPOX could be enhanced during forensic practice.

The value of genomic variant ClinVar submissions from clinical providers: Beyond the addition of novel variants

With the increasing use of clinical genomic testing across broad medical disciplines, the need for data sharing and curation efforts to improve variant interpretation is paramount. The National Center for Biotechnology Information (NCBI) ClinVar database facilitates these efforts by serving as a repository for clinical assertions about genomic variants and associations with disease. Most variant submissions are from clinical laboratories, which may lack clinical details. Laboratories may also choose not to submit all variants. Clinical providers can contribute to variant interpretation improvements by submitting variants to ClinVar with their own assertions and supporting evidence. The medical genetics team at Geisinger's Autism & Developmental Medicine Institute routinely reviews the clinical significance of all variants obtained through clinical genomic testing, using published ACMG/AMP guidelines, clinical correlation, and post-test clinical data. We describe the submission of 148 sequence and 155 copy number variants to ClinVar as "provider interpretations." Of these, 192 (63.4%) were novel to ClinVar. Detailed clinical data were provided for 298 (98.3%), and when available, segregation data and follow-up clinical correlation or testing was included. This contribution marks the first large-scale submission from a neurodevelopmental clinical setting and illustrates the importance of clinical providers in collaborative efforts to improve variant interpretation.

Identification of Novel Congenital Heart Disease Candidate Genes Using Chromosome Microarray

While the majority of patients have isolated heart disease, congenital heart disease (CHD) may be associated with other congenital anomalies or syndromes. Our institution utilizes chromosomal microarray (CMA) to identify chromosomal abnormalities, specifically copy number variations (CNVs). While CNVs have been associated with CHD, their direct impact on cardiac development remains unclear. This study sought to identify potential novel CHD candidate genes by comparing CNVs present in our institution's CHD population with those already recognized in the literature. A list of candidate genes was compiled from recent medical literature that utilized CMA. Records from neonatal cases at our institution over 10 years were reviewed. Genes identified from CMAs were compared with those reported in the literature and cross-referenced with the Online Mendelian Inheritance in Man catalog. We identified 375 CNVs reported in patients with CHD. At our institution between 2005 and 2015, 307 neonates with CHD had CMA. Of these, 77 patients (25%) had CNVs containing 832 unique candidate genes. 49 patients (16%) had isolated CHD with 353 candidate genes expressed within the CNVs, many of which were previously reported. However, there were 16 unique candidate genes identified that have been expressed with heart structure of the mouse knock-out models. Our findings demonstrate a high incidence of abnormal genes identified by CMA in CHD patients, including many CNVs of "unknown clinical significance". We conclude that a portion of these CNVs (including 16 genes expressed in the heart of the mouse knock-out models) could be candidate genes involved in CHD pathogenesis.

Semiconductor Sequencing Analysis of Chromosomal Copy Number Variations in Spontaneous Miscarriage

BACKGROUND Array CGH is the criterion standard for identifying copy number variations (CNV), but the restrictive requirement of DNA quality and relatively high cost prevent the use of this method as a general assay in hospitals in developing countries. Our principal objective was to determine whether the semiconductor sequencing platform (SSP) could be an alternative method in CNV detection for spontaneous miscarriage. MATERIAL AND METHODS A total of 443 spontaneous miscarriage samples were collected and subjected to low-coverage (0.1X) whole-genome analysis by SSP. These samples were verified by array CGH and 8 low-quality DNA samples were analyzed by SSP and validated by MLPA. RESULTS SSP detected 195 chromosomal numerical abnormalities, 74 CNVs, and 9 mosaicisms among the 435 samples. Among 74 CNV abnormalities, SSP detected an equal number (56) of CNVs 56 >1 Mb with array CGH. However, SSP missed more 6 cases CNVs <1 Mb than array CGH (12 vs. 18). SSP detected more mosaicisms than array CGH (9 vs. 7, p=0.5). Interestingly, SSP detected the mosaicism which had only 8% X monosomy, which was much lower than the minimal percentage of monosomy that was detected by array CGH. CONCLUSIONS SSP is of equivalent efficacy as array CGH in detecting CNVs >1 Mb, and performs better in identifying mosaicism. With the merits of low cost and less demand of input DNA, SSP is a good alternative for use in genetic diagnosis.

Use of clinical chromosomal microarray in Chinese patients with autism spectrum disorder-implications of a copy number variation involving DPP10

Background

Array comparative genomic hybridization (aCGH) is recommended as a first-tier genetic test for children with autism spectrum disorder (ASD). However, interpretation of results can often be challenging partly due to the fact that copy number variants (CNVs) in non-European ASD patients are not well studied. To address this literature gap, we report the CNV findings in a cohort of Chinese children with ASD.

Methods

DNA samples were obtained from 258 Chinese ASD patients recruited from a child assessment center between January 2011 and August 2014. aCGH was performed using NimbleGen-CGX-135k or Agilent-CGX 60k oligonucleotide array. Results were classified based on existing guidelines and literature.

Results

Ten pathogenic CNVs and one likely pathogenic CNV were found in nine patients, with an overall diagnostic yield of 3.5%. A 138 kb duplication involving 3′ exons of DPP10 (arr[GRCh37] 2q14.1(116534689_116672358)x3), reported to be associated with ASD, was identified in one patient (0.39%). The same CNV was reported as variant of uncertain significance (VUS) in DECIPHER database. Multiple individuals of typical development carrying a similar duplication were identified among our ancestry-matched control with a frequency of 6/653 (0.92%) as well as from literature and genomic databases.

Conclusions

The DPP10 duplication is likely a benign CNV polymorphism enriched in Southern Chinese with a population frequency of ~1%. This highlights the importance of using ancestry-matched controls in interpretation of aCGH findings.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-017-0136-x) contains supplementary material, which is available to authorized users.

Copy number variation in ALOX5 and PTGER1 is associated with NSAIDs-induced urticaria and/or angioedema

OBJECTIVE

Cross-intolerance to NSAIDs is a class of drug hypersensitivity reaction, of which NSAIDs-induced urticaria and/or angioedema (NIUA) are the most frequent clinical entities. They are considered to involve dysregulation of the arachidonic acid pathway; however, this mechanism has not been confirmed for NIUA. In this work, we assessed copy number variations (CNVs) in eight of the main genes involved in the arachidonic acid pathway and their possible genetic association with NIUA.

MATERIALS AND METHODS

CNVs in ALOX5, LTC4S, PTGS1, PTGS2, PTGER1, PTGER2, PTGER3, and PTGER4 were analyzed using TaqMan copy number assays. Genotyping was carried out by real-time quantitative PCR. Individual genotypes were assigned using the CopyCaller Software. Statistical analysis was carried out using GraphPad prism 5, PLINK, EPIDAT, and R version 3.1.2.

RESULTS AND CONCLUSION

A total of 151 cases and 139 controls were analyzed during the discovery phase and 148 cases and 140 controls were used for replication. CNVs in open reading frames were found for ALOX5, PTGER1, PTGER3, and PTGER4. Statistically significant differences in the CNV frequency between NIUA and controls were found for ALOX5 (Pc=0.017) and PTGER1 (Pc=1.22E-04). This study represents the first analysis showing an association between CNVs in exonic regions of ALOX5 and PTGER1 and NIUA. This suggests a role of CNVs in this pathology that should be explored further.

De novo large rare copy-number variations contribute to conotruncal heart disease in Chinese patients

Conotruncal heart anomalies (CTDs) are particularly prevalent congenital heart diseases (CHD) in Hong Kong. We surveyed large (>500 kb), rare (<1% frequency in controls) copy-number variations (CNVs) in Chinese patients with CTDs to identify potentially disease-causing variations. Adults who tested negative for 22q11.2 deletions were recruited from the adult CHD clinic in Hong Kong. Using a stringent calling criteria, high-confidence CNV calls were obtained, and a large control set comprising 3,987 Caucasian and 1,945 Singapore Chinese subjects was used to identify rare CNVs. Ten large rare CNVs were identified, and 3 in 108 individuals were confirmed to harbour de novo CNVs. All three patients were syndromic with a more complex phenotype, and each of these CNVs overlapped regions likely to be important in CHD. One was a 611 kb deletion at 17p13.3, telomeric to the Miller-Dieker syndrome (MDS) critical region, overlapping the NXN gene. Another was a 5 Mb deletion at 13q33.3, within a previously described critical region for CHD. A third CNV, previously unreported, was a large duplication at 2q22.3 overlapping the ZEB2 gene. The commonly reported 1q21.1 recurrent duplication was not observed in this Chinese cohort. We provide detailed phenotypic and genotypic descriptions of large rare genic CNVs that may represent CHD loci in the East Asian population. Larger samples of Chinese origin will be required to determine whether the genome-wide distribution differs from that found in predominantly European CHD cohorts.

A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease

The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.

The Current Landscape of Genetic Testing in Cardiovascular Malformations: Opportunities and Challenges

Human cardiovascular malformations (CVMs) frequently have a genetic contribution. Through the application of novel technologies, such as next-generation sequencing, DNA sequence variants associated with CVMs are being identified at a rapid pace. While clinicians are now able to offer testing with NGS gene panels or whole exome sequencing to any patient with a CVM, the interpretation of genetic variation remains problematic. Variable phenotypic expression, reduced penetrance, inconsistent phenotyping methods, and the lack of high-throughput functional testing of variants contribute to these challenges. This article elaborates critical issues that impact the decision to broadly implement clinical molecular genetic testing in CVMs. Major benefits of testing include establishing a genetic diagnosis, facilitating cost-effective screening of family members who may have subclinical disease, predicting recurrence risk in offsprings, enabling early diagnosis and anticipatory management of CV and non-CV disease phenotypes, predicting long-term outcomes, and facilitating the development of novel therapies aimed at disease improvement or prevention. Limitations include financial cost, psychosocial cost, and ambiguity of interpretation of results. Multiplex families and patients with syndromic features are two groups where disease causation could potentially be firmly established. However, these account for the minority of the overall CVM population, and there is increasing recognition that genotypes previously associated with syndromes also exist in patients who lack non-CV findings. In all circumstances, ongoing dialog between cardiologists and clinical geneticists will be needed to accurately interpret genetic testing and improve these patients’ health. This may be most effectively implemented by the creation and support of CV genetics services at centers committed to pursuing testing for patients.

Capturing phenotypes for precision medicine

Deep phenotyping followed by integrated computational analysis of genotype and phenotype is becoming ever more important for many areas of genomic diagnostics and translational research. The overwhelming majority of clinical descriptions in the medical literature are available only as natural language text, meaning that searching, analysis, and integration of medically relevant information in databases such as PubMed is challenging. The new journal Cold Spring Harbor Molecular Case Studies will require authors to select Human Phenotype Ontology terms for research papers that will be displayed alongside the manuscript, thereby providing a foundation for ontology-based indexing and searching of articles that contain descriptions of phenotypic abnormalities-an important step toward improving the ability of researchers and clinicians to get biomedical information that is critical for clinical care or translational research.

Diagnostic interpretation of array data using public databases and internet sources

The range of commercially available array platforms and analysis software packages is expanding and their utility is improving, making reliable detection of copy-number variants (CNVs) relatively straightforward. Reliable interpretation of CNV data, however, is often difficult and requires expertise. With our knowledge of the human genome growing rapidly, applications for array testing continuously broadening, and the resolution of CNV detection increasing, this leads to great complexity in interpreting what can be daunting data. Correct CNV interpretation and optimal use of the genotype information provided by single-nucleotide polymorphism probes on an array depends largely on knowledge present in various resources. In addition to the availability of host laboratories' own datasets and national registries, there are several public databases and Internet resources with genotype and phenotype information that can be used for array data interpretation. With so many resources now available, it is important to know which are fit-for-purpose in a diagnostic setting. We summarize the characteristics of the most commonly used Internet databases and resources, and propose a general data interpretation strategy that can be used for comparative hybridization, comparative intensity, and genotype-based array data.

Standards for Clinical Grade Genomic Databases

CONTEXT

Next-generation sequencing performed in a clinical environment must meet clinical standards, which requires reproducibility of all aspects of the testing. Clinical-grade genomic databases (CGGDs) are required to classify a variant and to assist in the professional interpretation of clinical next-generation sequencing. Applying quality laboratory standards to the reference databases used for sequence-variant interpretation presents a new challenge for validation and curation.

OBJECTIVES

To define CGGD and the categories of information contained in CGGDs and to frame recommendations for the structure and use of these databases in clinical patient care.

DESIGN

Members of the College of American Pathologists Personalized Health Care Committee reviewed the literature and existing state of genomic databases and developed a framework for guiding CGGD development in the future.

RESULTS

Clinical-grade genomic databases may provide different types of information. This work group defined 3 layers of information in CGGDs: clinical genomic variant repositories, genomic medical data repositories, and genomic medicine evidence databases. The layers are differentiated by the types of genomic and medical information contained and the utility in assisting with clinical interpretation of genomic variants. Clinical-grade genomic databases must meet specific standards regarding submission, curation, and retrieval of data, as well as the maintenance of privacy and security.

CONCLUSION

These organizing principles for CGGDs should serve as a foundation for future development of specific standards that support the use of such databases for patient care.

Building the foundation for genomics in precision medicine

Precision medicine has the potential to profoundly improve the practice of medicine. However, the advances required will take time to implement. Genetics is already being used to direct clinical decision-making and its contribution is likely to increase. To accelerate these advances, fundamental changes are needed in the infrastructure and mechanisms for data collection, storage and sharing. This will create a continuously learning health-care system with seamless cycling between clinical care and research. Patients must be educated about the benefits of sharing data. The building blocks for such a system are already forming and they will accelerate the adoption of precision medicine.

GenomeConnect: matchmaking between patients, clinical laboratories, and researchers to improve genomic knowledge

As the utility of genetic and genomic testing in healthcare grows, there is need for a high-quality genomic knowledge base to improve the clinical interpretation of genomic variants. Active patient engagement can enhance communication between clinicians, patients, and researchers, contributing to knowledge building. It also encourages data sharing by patients and increases the data available for clinicians to incorporate into individualized patient care, clinical laboratories to utilize in test interpretation, and investigators to use for research. GenomeConnect is a patient portal supported by the Clinical Genome Resource (ClinGen), providing an opportunity for patients to add to the knowledge base by securely sharing their health history and genetic test results. Data can be matched with queries from clinicians, laboratory personnel, and researchers to better interpret the results of genetic testing and build a foundation to support genomic medicine. Participation is online, allowing patients to contribute regardless of location. GenomeConnect supports longitudinal, detailed clinical phenotyping and robust "matching" among research and clinical communities. Phenotype data are gathered using online health questionnaires; genotype data are obtained from genetic test reports uploaded by participants and curated by staff. GenomeConnect empowers patients to actively participate in the improvement of genomic test interpretation and clinical utility.

Facilitating collaboration in rare genetic disorders through effective matchmaking in DECIPHER

DECIPHER (https://decipher.sanger.ac.uk) is a web‐based platform for secure deposition, analysis, and sharing of plausibly pathogenic genomic variants from well‐phenotyped patients suffering from genetic disorders. DECIPHER aids clinical interpretation of these rare sequence and copy‐number variants by providing tools for variant analysis and identification of other patients exhibiting similar genotype–phenotype characteristics. DECIPHER also provides mechanisms to encourage collaboration among a global community of clinical centers and researchers, as well as exchange of information between clinicians and researchers within a consortium, to accelerate discovery and diagnosis. DECIPHER has contributed to matchmaking efforts by enabling the global clinical genetics community to identify many previously undiagnosed syndromes and new disease genes, and has facilitated the publication of over 700 peer‐reviewed scientific publications since 2004. At the time of writing, DECIPHER contains anonymized data from ∼250 registered centers on more than 51,500 patients (∼18000 patients with consent for data sharing and ∼25000 anonymized records shared privately). In this paper, we describe salient features of the platform, with special emphasis on the tools and processes that aid interpretation, sharing, and effective matchmaking with other data held in the database and that make DECIPHER an invaluable clinical and research resource.

Genetic, environmental, and epigenetic factors involved in CAKUT

Congenital anomalies of the kidney and urinary tract (CAKUT) refer to a spectrum of structural renal malformations and are the leading cause of end-stage renal disease in children. The genetic diagnosis of CAKUT has proven to be challenging due to genetic and phenotypic heterogeneity and incomplete genetic penetrance. Monogenic causes of CAKUT have been identified using different approaches, including single gene screening, and gene panel and whole exome sequencing. The majority of the identified mutations, however, lack substantial evidence to support a pathogenic role in CAKUT. Copy number variants or single nucleotide variants that are associated with CAKUT have also been identified. Numerous studies support the influence of epigenetic and environmental factors on kidney development and the natural history of CAKUT, suggesting that the pathogenesis of this syndrome is multifactorial. In this Review we describe the current knowledge regarding the genetic susceptibility underlying CAKUT and the approaches used to investigate the genetic basis of CAKUT. We outline the associated environmental risk factors and epigenetic influences on CAKUT and discuss the challenges and strategies used to fully address the involvement and interplay of these factors in the pathogenesis of the disease.

Phenotyping: targeting genotype's rich cousin for diagnosis

There are many current and evolving tools to assist clinicians in their daily work of phenotyping. In medicine, the term 'phenotype' is usually taken to mean some deviation from normal morphology, physiology and behaviour. It is ascertained via history, examination and investigations, and a primary aim is diagnosis. Therefore, doctors are, by necessity, expert 'phenotypers'. There is an inherent and partially realised power in phenotypic information that when harnessed can improve patient care. Furthermore, phenotyping developments are increasingly important in an era of rapid advances in genomic technology. Fortunately, there is an expanding network of phenotyping tools that are poised for clinical translation. These tools will preferentially be implemented to mirror clinical workflows and to integrate with advances in genomic and information-sharing technologies. This will synergise with and augment the clinical acumen of medical practitioners. We outline key enablers of the ascertainment, integration and interrogation of clinical phenotype by using genetic diseases, particularly rare ones, as a theme. Successes from the test bed or rare diseases will support approaches to common disease.

Chromosome microarray testing for patients with congenital heart defects reveals novel disease causing loci and high diagnostic yield

Background

Congenital heart defects (CHD), as the most common congenital anomaly, have been reported to be frequently associated with pathogenic copy number variants (CNVs). Currently, patients with CHD are routinely offered chromosomal microarray (CMA) testing, but the diagnostic yield of CMA on CHD patients has not been extensively evaluated based on a large patient cohort. In this study, we retrospectively assessed the detected CNVs in a total of 514 CHD cases (a 422-case clinical cohort from Boston Children's Hospital (BCH) and a 92-case research cohort from Shanghai Children’s Medical Center (SCMC)) and conducted a genotype-phenotype analysis. Furthermore, genes encompassed in pathogenic/likely pathogenic CNVs were prioritized by integrating several tools and public data sources for novel CHD candidate gene identification.

Results

Based on the BCH cohort, the overall diagnostic yield of CMA testing for CHD patients was 12.8(pathogenic CNVs)-18.5% (pathogenic and likely pathogenic CNVs). The diagnostic yield of CMA for syndromic CHD was 14.1-20.6% (excluding aneuploidy cases), whereas the diagnostic yield for isolated CHD was 4.3-9.3%. Four recurrent genomic loci (4q terminal region, 15q11.2, 16p12.2 and Yp11.2) were more significantly enriched in cases than in controls. These regions are considered as novel CHD loci. We further identified 20 genes as the most likely novel CHD candidate genes through gene prioritization analysis.

Conclusion

The high clinical diagnostic yield of CMA in this study provides supportive evidence for CMA as the first-line genetic diagnostic tool for CHD patients. The CNVs detected in our study suggest a number of CHD candidate genes that warrant further investigation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1127) contains supplementary material, which is available to authorized users.

From genome to phenome-Simple inborn errors of metabolism as complex traits

Sporadically, patients with a proven defect in either mFAO or OXPHOS are described presenting with a metabolic profile and clinical phenotype expressing concurrent defects in both pathways. Biochemical linkages between both processes are tight. Therefore, it is striking that concurrent dysfunction of both systems occurs so infrequent. In this review, the linkages between OXPHOS and mFAO and the hypothesized processes responsible for concurrent problems in both systems are reviewed, both from the point of view of primary biochemical connections and secondary cellular responses, i.e. signaling pathways constituting nutrient-sensing networks. We propose that affected signaling pathways may play an important role in the phenomenon of concurrent defects. Recent data indicate that interference in the affected signaling pathways may resolve the pathological phenotype even though the primary enzyme deficiency persists. This offers new (unexpected) prospects for treatment of these inborn errors of metabolism. This article is part of a Special Issue entitled: From Genome to Function.

Annotating DNA variants is the next major goal for human genetics

Clinical genetic testing has undergone a dramatic transformation in the past two decades. Diagnostic laboratories that previously tested for well-established disease-causing DNA variants in a handful of genes have evolved into sequencing factories identifying thousands of variants of known and unknown medical consequence. Sorting out what does and does not cause disease in our genomes is the next great challenge in making genetics a central feature of healthcare. I propose that closing the gap in our ability to interpret variation responsible for Mendelian disorders provides a grand and unprecedented opportunity for geneticists. Human geneticists are well placed to coordinate a systematic evaluation of variants in collaboration with basic scientists and clinicians. Sharing of knowledge, data, methods, and tools will aid both researchers and healthcare workers in achieving their common goal of defining the pathogenic potential of variants. Generation of variant annotations will inform genetic testing and will deepen our understanding of gene and protein function, thereby aiding the search for molecular targeted therapies.

The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.

DECIPHER: database for the interpretation of phenotype-linked plausibly pathogenic sequence and copy-number variation

The DECIPHER database (https://decipher.sanger.ac.uk/) is an accessible online repository of genetic variation with associated phenotypes that facilitates the identification and interpretation of pathogenic genetic variation in patients with rare disorders. Contributing to DECIPHER is an international consortium of >200 academic clinical centres of genetic medicine and ≥1600 clinical geneticists and diagnostic laboratory scientists. Information integrated from a variety of bioinformatics resources, coupled with visualization tools, provides a comprehensive set of tools to identify other patients with similar genotype–phenotype characteristics and highlights potentially pathogenic genes. In a significant development, we have extended DECIPHER from a database of just copy-number variants to allow upload, annotation and analysis of sequence variants such as single nucleotide variants (SNVs) and InDels. Other notable developments in DECIPHER include a purpose-built, customizable and interactive genome browser to aid combined visualization and interpretation of sequence and copy-number variation against informative datasets of pathogenic and population variation. We have also introduced several new features to our deposition and analysis interface. This article provides an update to the DECIPHER database, an earlier instance of which has been described elsewhere [Swaminathan et al. (2012) DECIPHER: web-based, community resource for clinical interpretation of rare variants in developmental disorders. Hum. Mol. Genet., 21, R37–R44].

BBGRE: brain and body genetic resource exchange

Studies of copy number variation (genomic imbalance) are providing insight into both complex and Mendelian genetic disorders. Array comparative genomic hybridization (array CGH), a tool for detecting copy number variants at a resolution previously unattainable in clinical diagnostics, is increasingly used as a first-line test at clinical genetics laboratories. Many copy number variants are of unknown significance; correlation and comparison with other patients will therefore be essential for interpretation. We present a resource for clinicians and researchers to identify specific copy number variants and associated phenotypes in patients from a single catchment area, tested using array CGH at the SE Thames Regional Genetics Centre, London. User-friendly searching is available, with links to external resources, providing a powerful tool for the elucidation of gene function. We hope to promote research by facilitating interactions between researchers and patients. The BBGRE (Brain and Body Genetic Resource Exchange) resource can be accessed at the following website: http://bbgre.org DATABASE URL: http://bbgre.org.

Getting ready for the Human Phenome Project: the 2012 forum of the Human Variome Project

A forum of the Human Variome Project (HVP) was held as a satellite to the 2012 Annual Meeting of the American Society of Human Genetics in San Francisco, California. The theme of this meeting was "Getting Ready for the Human Phenome Project." Understanding the genetic contribution to both rare single-gene "Mendelian" disorders and more complex common diseases will require integration of research efforts among many fields and better defined phenotypes. The HVP is dedicated to bringing together researchers and research populations throughout the world to provide the resources to investigate the impact of genetic variation on disease. To this end, there needs to be a greater sharing of phenotype and genotype data. For this to occur, many databases that currently exist will need to become interoperable to allow for the combining of cohorts with similar phenotypes to increase statistical power for studies attempting to identify novel disease genes or causative genetic variants. Improved systems and tools that enhance the collection of phenotype data from clinicians are urgently needed. This meeting begins the HVP's effort toward this important goal.

The facial evolution: looking backward and moving forward

Three-dimensional (3D) facial analysis is ideal for high-resolution, nonionizing, noninvasive objective, high-throughput phenotypic, and phenomic studies. It is a natural complement to (epi)genetic technologies to facilitate advances in the understanding of rare and common diseases. The face is uniquely reflective of the primordial tissues, and there is evidence supporting the application of 3D facial analysis to the investigation of variation and disease including studies showing that the face can reflect systemic health, provides diagnostic clues to disorders, and that facial variation reflects biological pathways. In addition, facial variation has been related to evolutionary factors. The purpose of this review is to look backward to suggest that knowledge of human evolution supports, and may instruct, the application and interpretation of studies of facial morphology for documentation of human variation and investigation of its relationships with health and disease. Furthermore, in the context of advances of deep phenotyping and data integration, to look forward to suggest approaches to scalable implementation of facial analysis, and to suggest avenues for future research and clinical application of this technology.

Phenotypic overlap in the contribution of individual genes to CNV pathogenicity revealed by cross-species computational analysis of single-gene mutations in humans, mice and zebrafish

Numerous disease syndromes are associated with regions of copy number variation (CNV) in the human genome and, in most cases, the pathogenicity of the CNV is thought to be related to altered dosage of the genes contained within the affected segment. However, establishing the contribution of individual genes to the overall pathogenicity of CNV syndromes is difficult and often relies on the identification of potential candidates through manual searches of the literature and online resources. We describe here the development of a computational framework to comprehensively search phenotypic information from model organisms and single-gene human hereditary disorders, and thus speed the interpretation of the complex phenotypes of CNV disorders. There are currently more than 5000 human genes about which nothing is known phenotypically but for which detailed phenotypic information for the mouse and/or zebrafish orthologs is available. Here, we present an ontology-based approach to identify similarities between human disease manifestations and the mutational phenotypes in characterized model organism genes; this approach can therefore be used even in cases where there is little or no information about the function of the human genes. We applied this algorithm to detect candidate genes for 27 recurrent CNV disorders and identified 802 gene-phenotype associations, approximately half of which involved genes that were previously reported to be associated with individual phenotypic features and half of which were novel candidates. A total of 431 associations were made solely on the basis of model organism phenotype data. Additionally, we observed a striking, statistically significant tendency for individual disease phenotypes to be associated with multiple genes located within a single CNV region, a phenomenon that we denote as pheno-clustering. Many of the clusters also display statistically significant similarities in protein function or vicinity within the protein-protein interaction network. Our results provide a basis for understanding previously un-interpretable genotype-phenotype correlations in pathogenic CNVs and for mobilizing the large amount of model organism phenotype data to provide insights into human genetic disorders.

Women's experiences receiving abnormal prenatal chromosomal microarray testing results

PURPOSE

Genomic microarrays can detect copy-number variants not detectable by conventional cytogenetics. This technology is diffusing rapidly into prenatal settings even though the clinical implications of many copy-number variants are currently unknown. We conducted a qualitative pilot study to explore the experiences of women receiving abnormal results from prenatal microarray testing performed in a research setting.

METHODS

Participants were a subset of women participating in a multicenter prospective study "Prenatal Cytogenetic Diagnosis by Array-based Copy Number Analysis." Telephone interviews were conducted with 23 women receiving abnormal prenatal microarray results.

RESULTS

We found that five key elements dominated the experiences of women who had received abnormal prenatal microarray results: an offer too good to pass up, blindsided by the results, uncertainty and unquantifiable risks, need for support, and toxic knowledge.

CONCLUSION

As prenatal microarray testing is increasingly used, uncertain findings will be common, resulting in greater need for careful pre- and posttest counseling, and more education of and resources for providers so they can adequately support the women who are undergoing testing.

Deep phenotyping for precision medicine

In medical contexts, the word "phenotype" is used to refer to some deviation from normal morphology, physiology, or behavior. The analysis of phenotype plays a key role in clinical practice and medical research, and yet phenotypic descriptions in clinical notes and medical publications are often imprecise. Deep phenotyping can be defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The emerging field of precision medicine aims to provide the best available care for each patient based on stratification into disease subclasses with a common biological basis of disease. The comprehensive discovery of such subclasses, as well as the translation of this knowledge into clinical care, will depend critically upon computational resources to capture, store, and exchange phenotypic data, and upon sophisticated algorithms to integrate it with genomic variation, omics profiles, and other clinical information. This special issue of Human Mutation offers a number of articles describing computational solutions for current challenges in deep phenotyping, including semantic and technical standards for phenotype and disease data, digital imaging for facial phenotype analysis, model organism phenotypes, and databases for correlating phenotypes with genomic variation.

The laboratory-clinician team: a professional call to action to improve communication and collaboration for optimal patient care in chromosomal microarray testing

The International Standards for Cytogenomic Arrays (ISCA) Consortium is a worldwide collaborative effort dedicated to optimizing patient care by improving the quality of chromosomal microarray testing. The primary effort of the ISCA Consortium has been the development of a database of copy number variants (CNVs) identified during the course of clinical microarray testing. This database is a powerful resource for clinicians, laboratories, and researchers, and can be utilized for a variety of applications, such as facilitating standardized interpretations of certain CNVs across laboratories or providing phenotypic information for counseling purposes when published data is sparse. A recognized limitation to the clinical utility of this database, however, is the quality of clinical information available for each patient. Clinical genetic counselors are uniquely suited to facilitate the communication of this information to the laboratory by virtue of their existing clinical responsibilities, case management skills, and appreciation of the evolving nature of scientific knowledge. We intend to highlight the critical role that genetic counselors play in ensuring optimal patient care through contributing to the clinical utility of the ISCA Consortium's database, as well as the quality of individual patient microarray reports provided by contributing laboratories. Current tools, paper and electronic forms, created to maximize this collaboration are shared. In addition to making a professional commitment to providing complete clinical information, genetic counselors are invited to become ISCA members and to become involved in the discussions and initiatives within the Consortium.