Australian Genomics: Outcomes of a 5-year national program to accelerate the integration of genomics in healthcare.

Australian Genomics is a national collaborative partnership of more than 100 organizations piloting a whole-of-system approach to integrating genomics into healthcare, based on federation principles. In the first five years of operation, Australian Genomics has evaluated the outcomes of genomic testing in more than 5,200 individuals across 19 rare disease and cancer flagship studies. Comprehensive analyses of the health economic, policy, ethical, legal, implementation and workforce implications of incorporating genomics in the Australian context have informed evidence-based change in policy and practice, resulting in national government funding and equity of access for a range of genomic tests. Simultaneously, Australian Genomics has built national skills, infrastructure, policy, and data resources to enable effective data sharing to drive discovery research and support improvements in clinical genomic delivery.

Moving Genomics into the Clinic: Platforms for Implementing Clinical Genomic Data-Sharing in Ways That Address Ethical, Legal and Social Implications.

This section explores the challenges involved in translating genomic research into genomic medicine. A number of priorities have been identified in the Australian National Health Genomics Framework for addressing these challenges. Responsible collection, storage, use and management of genomic data is one of these priorities, and is the primary theme of this section. The recent release of Genomical, an Australian data-sharing platform, is used as a case study to illustrate the type of assistance that can be provided to the health care sector in addressing this priority. The section first describes the National Framework and other drivers involved in the move towards genomic medicine. The section then examines key ethical, legal and social factors at play in genomics, with particular focus on privacy and consent. Finally, the section examines how Genomical is being used to help ensure that the move towards genomic medicine is ethically, legally and socially sound and that it optimises advances in both genomic and information technology.

MTR3D: identifying regions within protein tertiary structures under purifying selection.

The identification of disease-causal variants is non-trivial. By mapping population variation from over 448,000 exome and genome sequences to over 81,000 experimental structures and homology models of the human proteome, we have calculated both regional intolerance to missense variation (Missense Tolerance Ratio, MTR), using a sliding window of 21-41 codons, and introduce a new 3D spatial intolerance to missense variation score (3D Missense Tolerance Ratio, MTR3D), using spheres of 5-8 Å. We show that the MTR3D is less biased by regions with limited data and more accurately identifies regions under purifying selection than estimates relying on the sequence alone. Intolerant regions were highly enriched for both ClinVar pathogenic and COSMIC somatic missense variants (Mann-Whitney U test P < 2.2 × 10-16). Further, we combine sequence- and spatial-based scores to generate a consensus score, MTRX, which distinguishes pathogenic from benign variants more accurately than either score separately (AUC = 0.85). The MTR3D server enables easy visualisation of population variation, MTR, MTR3D and MTRX scores across the entire gene and protein structure for >17,000 human genes and >42,000 alternative alternate transcripts, including both Ensembl and RefSeq transcripts. MTR3D is freely available by user-friendly web-interface and API at http://biosig.unimelb.edu.au/mtr3d/.

aCLImatise: automated generation of tool definitions for bioinformatics workflows.

SUMMARY: aCLImatise is a utility for automatically generating tool definitions compatible with bioinformatics workflow languages, by parsing command-line help output. aCLImatise also has an associated database called the aCLImatise Base Camp, which provides thousands of pre-computed tool definitions. AVAILABILITY AND IMPLEMENTATION: The latest aCLImatise source code is available within a GitHub organisation, under the GPL-3.0 license: https://github.com/aCLImatise. In particular, documentation for the aCLImatise Python package is available at https://aclimatise.github.io/CliHelpParser/, and the aCLImatise Base Camp is available at https://aclimatise.github.io/BaseCamp/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of computational programs to predict HLA genotypes from genomic sequencing data.

Motivation: Despite being essential for numerous clinical and research applications, high-resolution human leukocyte antigen (HLA) typing remains challenging and laboratory tests are also time-consuming and labour intensive. With next-generation sequencing data becoming widely accessible, on-demand in silico HLA typing offers an economical and efficient alternative. Results: In this study we evaluate the HLA typing accuracy and efficiency of five computational HLA typing methods by comparing their predictions against a curated set of > 1000 published polymerase chain reaction-derived HLA genotypes on three different data sets (whole genome sequencing, whole exome sequencing and transcriptomic sequencing data). The highest accuracy at clinically relevant resolution (four digits) we observe is 81% on RNAseq data by PHLAT and 99% accuracy by OptiType when limited to Class I genes only. We also observed variability between the tools for resource consumption, with runtime ranging from an average of 5 h (HLAminer) to 7 min (seq2HLA) and memory from 12.8 GB (HLA-VBSeq) to 0.46 GB (HLAminer) per sample. While a minimal coverage is required, other factors also determine prediction accuracy and the results between tools do not correlate well. Therefore, by combining tools, there is the potential to develop a highly accurate ensemble method that is able to deliver fast, economical HLA typing from existing sequencing data.

An Emerging Female Phenotype with Loss-of-Function Mutations in the Aristaless-Related Homeodomain Transcription Factor ARX.

The devastating clinical presentation of X-linked lissencephaly with abnormal genitalia (XLAG) is invariably caused by loss-of-function mutations in the Aristaless-related homeobox (ARX) gene. Mutations in this X-chromosome gene contribute to intellectual disability (ID) with co-morbidities including seizures and movement disorders such as dystonia in affected males. The detection of affected females with mutations in ARX is increasing. We present a family with multiple affected individuals, including two females. Two male siblings presenting with XLAG were deceased prior to full-term gestation or within the first few weeks of life. Of the two female siblings, one presented with behavioral disturbances, mild ID, a seizure disorder, and complete agenesis of the corpus callosum (ACC), similar to the mother's phenotype. A novel insertion mutation in Exon 2 of ARX was identified, c.982delCinsTTT predicted to cause a frameshift at p.(Q328Ffs* 37). Our finding is consistent with loss-of-function mutations in ARX causing XLAG in hemizygous males and extends the findings of ID and seizures in heterozygous females. We review the reported phenotypes of females with mutations in ARX and highlight the importance of screening ARX in male and female patients with ID, seizures, and in particular with complete ACC.

A prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders.

PURPOSE: To prospectively evaluate the diagnostic and clinical utility of singleton whole-exome sequencing (WES) as a first-tier test in infants with suspected monogenic disease. METHODS: Singleton WES was performed as a first-tier sequencing test in infants recruited from a single pediatric tertiary center. This occurred in parallel with standard investigations, including single- or multigene panel sequencing when clinically indicated. The diagnosis rate, clinical utility, and impact on management of singleton WES were evaluated. RESULTS: Of 80 enrolled infants, 46 received a molecular genetic diagnosis through singleton WES (57.5%) compared with 11 (13.75%) who underwent standard investigations in the same patient group. Clinical management changed following exome diagnosis in 15 of 46 diagnosed participants (32.6%). Twelve relatives received a genetic diagnosis following cascade testing, and 28 couples were identified as being at high risk of recurrence in future pregnancies. CONCLUSIONS: This prospective study provides strong evidence for increased diagnostic and clinical utility of singleton WES as a first-tier sequencing test for infants with a suspected monogenic disorder. Singleton WES outperformed standard care in terms of diagnosis rate and the benefits of a diagnosis, namely, impact on management of the child and clarification of reproductive risks for the extended family in a timely manner.Genet Med 18 11, 1090-1096.

Use of copy number deletion polymorphisms to assess DNA chimerism.

BACKGROUND: We describe a novel approach that harnesses the ubiquity of copy number deletion polymorphisms in human genomes to definitively detect and quantify chimeric DNA in clinical samples. Unlike other molecular approaches to chimerism analysis, the copy number deletion (CND) method targets genomic loci (>50 base pairs in length) that are wholly absent from wild-type (i.e., self) background DNA sequences in a sex-independent manner. METHODS: Bespoke quantitative PCR (qPCR) CND assays were developed and validated using a series of DNA standards and chimeric plasma DNA samples collected from 2 allogeneic kidney transplant recipients and 12 pregnant women. Assay performance and informativeness were assessed using appropriate statistical methods. RESULTS: The CND qPCR assays showed high sensitivity, precision, and reliability for linear quantification of DNA chimerism down to 16 genomic equivalents (i.e., 106 pg). Fetal fraction (%) in 12 singleton male pregnancies was calculated using the CND qPCR approach, which showed closer agreement with single-nucleotide polymorphism-based massively parallel sequencing than the SRY (sex determining region Y) (Y chromosome) qPCR assay. The latter consistently underestimated the fetal fraction relative to the other methods. We also were able to measure biological changes in plasma nonself DNA concentrations in 2 renal transplant recipients. CONCLUSIONS: The CND qPCR technique is suitable for measurement of chimerism for monitoring of rejection in allogeneic organ transplantation and quantification of the cell-free fetal DNA fraction in maternal plasma samples used for noninvasive prenatal genetic testing.

Future-proofing genomic data and consent management: a comprehensive review of technology innovations.

Genomic information is increasingly used to inform medical treatments and manage future disease risks. However, any personal and societal gains must be carefully balanced against the risk to individuals contributing their genomic data. Expanding our understanding of actionable genomic insights requires researchers to access large global datasets to capture the complexity of genomic contribution to diseases. Similarly, clinicians need efficient access to a patient's genome as well as population-representative historical records for evidence-based decisions. Both researchers and clinicians hence rely on participants to consent to the use of their genomic data, which in turn requires trust in the professional and ethical handling of this information. Here, we review existing and emerging solutions for secure and effective genomic information management, including storage, encryption, consent, and authorization that are needed to build participant trust. We discuss recent innovations in cloud computing, quantum-computing-proof encryption, and self-sovereign identity. These innovations can augment key developments from within the genomics community, notably GA4GH Passports and the Crypt4GH file container standard. We also explore how decentralized storage as well as the digital consenting process can offer culturally acceptable processes to encourage data contributions from ethnic minorities. We conclude that the individual and their right for self-determination needs to be put at the center of any genomics framework, because only on an individual level can the received benefits be accurately balanced against the risk of exposing private information.

The cost of reducing starting RNA quantity for Illumina BeadArrays: a bead-level dilution experiment.

BACKGROUND: The demands of microarray expression technologies for quantities of RNA place a limit on the questions they can address. As a consequence, the RNA requirements have reduced over time as technologies have improved. In this paper we investigate the costs of reducing the starting quantity of RNA for the Illumina BeadArray platform. This we do via a dilution data set generated from two reference RNA sources that have become the standard for investigations into microarray and sequencing technologies. RESULTS: We find that the starting quantity of RNA has an effect on observed intensities despite the fact that the quantity of cRNA being hybridized remains constant. We see a loss of sensitivity when using lower quantities of RNA, but no great rise in the false positive rate. Even with 10 ng of starting RNA, the positive results are reliable although many differentially expressed genes are missed. We see that there is some scope for combining data from samples that have contributed differing quantities of RNA, but note also that sample sizes should increase to compensate for the loss of signal-to-noise when using low quantities of starting RNA. CONCLUSIONS: The BeadArray platform maintains a low false discovery rate even when small amounts of starting RNA are used. In contrast, the sensitivity of the platform drops off noticeably over the same range. Thus, those conducting experiments should not opt for low quantities of starting RNA without consideration of the costs of doing so. The implications for experimental design, and the integration of data from different starting quantities, are complex.

Tissue-specific splicing factor gene expression signatures.

The alternative splicing code that controls and coordinates the transcriptome in complex multicellular organisms remains poorly understood. It has long been argued that regulation of alternative splicing relies on combinatorial interactions between multiple proteins, and that tissue-specific splicing decisions most likely result from differences in the concentration and/or activity of these proteins. However, large-scale data to systematically address this issue have just recently started to become available. Here we show that splicing factor gene expression signatures can be identified that reflect cell type and tissue-specific patterns of alternative splicing. We used a computational approach to analyze microarray-based gene expression profiles of splicing factors from mouse, chimpanzee and human tissues. Our results show that brain and testis, the two tissues with highest levels of alternative splicing events, have the largest number of splicing factor genes that are most highly differentially expressed. We further identified SR protein kinases and small nuclear ribonucleoprotein particle (snRNP) proteins among the splicing factor genes that are most highly differentially expressed in a particular tissue. These results indicate the power of generating signature-based predictions as an initial computational approach into a global view of tissue-specific alternative splicing regulation.

A head-to-head evaluation of the diagnostic efficacy and costs of trio versus singleton exome sequencing analysis.

Diagnostic exome sequencing (ES) can be performed on the proband only (singleton; sES) or with additional samples, often including both biological parents with the proband (trio; tES). In this study we sought to compare the efficiencies of exome sequencing (ES) by trio (tES) versus singleton (sES) approach, determine costs, and identify factors to consider when deciding on optimal implementation strategies for the diagnosis of monogenic disorders. We undertook ES in 30 trios and analysed each proband's sES and tES data in parallel. Two teams were randomly allocated to either sES or tES analysis for each case and blinded to each other's work. Each task was timed and cost analyses were based on time taken and diagnostic yield. We modelled three scenarios to determine the factors to consider in the implementation of tES. sES diagnosed 11/30 (36.7%) cases and tES identified one additional diagnosis (12/30 (40.0%)). tES obviated the need for Sanger segregation, reduced the number of variants for curation, and had lower cost-per-diagnosis when considering analysis alone. When sequencing costs were included, tES nearly doubled the cost of sES. Reflexing to tES in those who remain undiagnosed after sES was cost-saving over tES in all as first-line. This approach requires a large differential in diagnostic yield between sES and tES for maximal benefit given current sequencing costs. tES may be preferable when scaling up laboratory throughput due to efficiency gains and opportunity cost considerations. Our findings are relevant to clinicians, laboratories and health services considering tES over sES.

MMASS: an optimized array-based method for assessing CpG island methylation.

We describe an optimized microarray method for identifying genome-wide CpG island methylation called microarray-based methylation assessment of single samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimized combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared with a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison with previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation.

Metabolic consequences of p300 gene deletion in human colon cancer cells.

Metabolite profiling using (1)H nuclear magnetic resonance (NMR) spectroscopy was used to investigate the metabolic changes associated with deletion of the gene for the transcriptional coactivator p300 in the human colon carcinoma cell line HCT116. Multivariate statistical methods were used to distinguish between metabolite patterns that were dependent on cell growth conditions and those that were specifically associated with loss of p300 function. In the absence of serum, wild-type cells showed slower growth, which was accompanied by a marked decrease in phosphocholine concentration, which was not observed in otherwise isogenic cell lines lacking p300. In the presence of serum, several metabolites were identified as being significantly different between the two cell types, including glutamate and glutamine, a nicotinamide-related compound and glycerophosphocholine (GPC). However, in the absence of serum, these metabolites, with the exception of GPC, were not significantly different, leading us to conclude that most of these changes were context dependent. Transcript profiling, using DNA microarrays, showed changes in the levels of transcripts for several enzymes involved in choline metabolism, which might explain the change in GPC concentration. Localized in vivo (1)H NMR measurements on the tumors formed following s.c. implantation of these cells into mice showed an increase in the intensity of the peak from choline-containing compounds in the p300(-) tumors. These data show that NMR-based metabolite profiling has sufficient sensitivity to identify the metabolic consequences of p300 gene deletion in tumor cells in vitro and in vivo.

Exome sequencing has higher diagnostic yield compared to simulated disease-specific panels in children with suspected monogenic disorders.

As test costs decline, whole-exome sequencing (WES) has become increasingly used for clinical diagnosis, and now represents the primary alternative to gene panel testing for patients with a suspected genetic disorder. We sought to compare the diagnostic yield of singleton-WES with simulated application of commercial gene panels in children suspected of having a genetically heterogeneous condition. Recruitment, singleton-WES and phenotype-driven variant analysis was completed for 145 paediatric patients. At recruitment, clinicians were required to propose commercial gene panel tests as an alternative to WES and nominate a phenotype-driven candidate gene list. In WES-diagnosed children, three commercial options for each proposed panel were identified and evaluated for hypothetical diagnostic yield assuming 100% analytical sensitivity and specificity. We compared the price of WES with the least costly panel in WES-diagnosed children. In WES-undiagnosed children, we evaluated the exonic coverage of their phenotype-driven gene list using aggregate data. WES diagnoses were made in genes not included in at least one-of-three commercial panels in 42% of cases. Had a panel been selected instead, 23% of WES-diagnosed children would not have been diagnosed. In 26% of cases, the least costly panel option would have been more expensive than WES. Evaluation of WES coverage found that at the most stringent level of 20× coverage, the likelihood of missing a clinically relevant variant in a candidate gene list was maximally 8%. The broader coverage of WES makes it a superior alternative to gene panel testing at similar financial cost for children with suspected complex monogenic phenotypes.

Missing channels in two-colour microarray experiments: combining single-channel and two-channel data.

BACKGROUND: There are mechanisms, notably ozone degradation, that can damage a single channel of two-channel microarray experiments. Resulting analyses therefore often choose between the unacceptable inclusion of poor quality data or the unpalatable exclusion of some (possibly a lot of) good quality data along with the bad. Two such approaches would be a single channel analysis using some of the data from all of the arrays, and an analysis of all of the data, but only from unaffected arrays. In this paper we examine a 'combined' approach to the analysis of such affected experiments that uses all of the unaffected data. RESULTS: A simulation experiment shows that while a single channel analysis performs relatively well when the majority of arrays are affected, and excluding affected arrays performs relatively well when few arrays are affected (as would be expected in both cases), the combined approach out-performs both. There are benefits to actively estimating the key-parameter of the approach, but whether these compensate for the increased computational cost and complexity over just setting that parameter to take a fixed value is not clear. Inclusion of ozone-affected data results in poor performance, with a clear spatial effect in the damage being apparent. CONCLUSION: There is no need to exclude unaffected data in order to remove those which are damaged. The combined approach discussed here is shown to out-perform more usual approaches, although it seems that if the damage is limited to very few arrays, or extends to very nearly all, then the benefits will be limited. In other circumstances though, large improvements in performance can be achieved by adopting such an approach.

A clinically driven variant prioritization framework outperforms purely computational approaches for the diagnostic analysis of singleton WES data.

Rapid identification of clinically significant variants is key to the successful application of next generation sequencing technologies in clinical practice. The Melbourne Genomics Health Alliance (MGHA) variant prioritization framework employs a gene prioritization index based on clinician-generated a priori gene lists, and a variant prioritization index (VPI) based on rarity, conservation and protein effect. We used data from 80 patients who underwent singleton whole exome sequencing (WES) to test the ability of the framework to rank causative variants highly, and compared it against the performance of other gene and variant prioritization tools. Causative variants were identified in 59 of the patients. Using the MGHA prioritization framework the average rank of the causative variant was 2.24, with 76% ranked as the top priority variant, and 90% ranked within the top five. Using clinician-generated gene lists resulted in ranking causative variants an average of 8.2 positions higher than prioritization based on variant properties alone. This clinically driven prioritization approach significantly outperformed purely computational tools, placing a greater proportion of causative variants top or in the top 5 (permutation P-value=0.001). Clinicians included 40 of the 49 WES diagnoses in their a priori list of differential diagnoses (81%). The lists generated by PhenoTips and Phenomizer contained 14 (29%) and 18 (37%) of these diagnoses respectively. These results highlight the benefits of clinically led variant prioritization in increasing the efficiency of singleton WES data analysis and have important implications for developing models for the funding and delivery of genomic services.

Real-world utility of whole exome sequencing with targeted gene analysis for focal epilepsy.

OBJECTIVE: Driven by advances in genomic technology and reduction in costs, next-generation sequencing (NGS) is venturing into routine clinical care. The 'real-world' clinical utility of NGS remains to be determined in focal epilepsies, which account for 60% of all epilepsies and for which the importance of genetic factors is just beginning to emerge. We investigated the diagnostic yield and management implications of whole exome sequencing (WES)-based screening of selected genes in the routine care of common focal epilepsies suspected to have a genetic basis. METHODS: We performed WES, followed by targeted analysis of 64 epilepsy genes, on 40 consecutive children and adults enrolled prospectively from routine clinical practice who had MRI-negative focal epilepsy and a family history of febrile seizures or any type of epilepsy in at least one first- or second-degree relative. Exclusion criteria were previous genetic testing, severe intellectual disability and benign focal epilepsies of childhood. RESULTS: 5/40 (12.5%) patients had a pathogenic or likely pathogenic variant, detected in SCN1A, DEPDC5, PCDH19, GABRG2 or NPRL2. Identifying a pathogenic SCN1A variant in a patient with drug-resistant epilepsy prompted to halt presurgical investigations due to concern of unfavorable post-surgical outcome. It also led in the same patient to discontinue long-standing carbamazepine therapy (a potentially aggravating drug in epilepsies due to SCN1A mutations), resulting in complete seizure control. Patients with pathogenic or likely pathogenic variants had a younger median age of seizure onset (range) compared to those without [18 months (8 months-18 years) vs 18 years (18 months-70 years), p=0.02]. SIGNIFICANCE: Our data demonstrate that WES with targeted gene analysis is an effective diagnostic tool for patients with common focal epilepsies in whom a genetic etiology is suspected. It can also influence clinical decision-making, including antiepileptic drug selection and consideration of epilepsy surgery, hence supporting its incorporation in the routine clinical care of this patient group.

Diagnostic Impact and Cost-effectiveness of Whole-Exome Sequencing for Ambulant Children With Suspected Monogenic Conditions.

Importance: Optimal use of whole-exome sequencing (WES) in the pediatric setting requires an understanding of who should be considered for testing and when it should be performed to maximize clinical utility and cost-effectiveness. Objectives: To investigate the impact of WES in sequencing-naive children suspected of having a monogenic disorder and evaluate its cost-effectiveness if WES had been available at different time points in their diagnostic trajectory. Design, Setting, and Participants: This prospective study was part of the Melbourne Genomics Health Alliance demonstration project. At the ambulatory outpatient clinics of the Victorian Clinical Genetics Services at the Royal Children's Hospital, Melbourne, Australia, children older than 2 years suspected of having a monogenic disorder were prospectively recruited from May 1 through November 30, 2015, by clinical geneticists after referral from general and subspecialist pediatricians. All children had nondiagnostic microarrays and no prior single-gene or panel sequencing. Exposures: All children underwent singleton WES with targeted phenotype-driven analysis. Main Outcomes and Measures: The study examined the clinical utility of a molecular diagnosis and the cost-effectiveness of alternative diagnostic trajectories, depending on timing of WES. Results: Of 61 children originally assessed, 44 (21 [48%] male and 23 [52%] female) aged 2 to 18 years (mean age at initial presentation, 28 months; range, 0-121 months) were recruited, and a diagnosis was achieved in 23 (52%) by singleton WES. The diagnoses were unexpected in 8 of 23 (35%), and clinical management was altered in 6 of 23 (26%). The mean duration of the diagnostic odyssey was 6 years, with each child having a mean of 19 tests and 4 clinical genetics and 4 nongenetics specialist consultations, and 26 (59%) underwent a procedure while under general anesthetic for diagnostic purposes. Economic analyses of the diagnostic trajectory identified that WES performed at initial tertiary presentation resulted in an incremental cost savings of A$9020 (US$6838) per additional diagnosis (95% CI, A$4304-A$15 404 [US$3263-US$11 678]) compared with the standard diagnostic pathway. Even if WES were performed at the first genetics appointment, there would be an incremental cost savings of A$5461 (US$4140) (95% CI, A$1433-A$10 557 [US$1086- US$8004]) per additional diagnosis compared with the standard diagnostic pathway. Conclusions and Relevance: Singleton WES in children with suspected monogenic conditions has high diagnostic yield, and cost-effectiveness is maximized by early application in the diagnostic pathway. Pediatricians should consider early referral of children with undiagnosed syndromes to clinical geneticists.