Evaluation of the diagnostic accuracy of exome sequencing and its impact on diagnostic thinking for patients with rare disease in a publicly funded health care system: A prospective cohort study.

PURPOSE: To evaluate the diagnostic utility of publicly funded clinical exome sequencing (ES) for patients with suspected rare genetic diseases. METHODS: We prospectively enrolled 297 probands who met eligibility criteria and received ES across 5 sites in Ontario, Canada, and extracted data from medical records and clinician surveys. Using the Fryback and Thornbury Efficacy Framework, we assessed diagnostic accuracy by examining laboratory interpretation of results and assessed diagnostic thinking by examining the clinical interpretation of results and whether clinical-molecular diagnoses would have been achieved via alternative hypothetical molecular tests. RESULTS: Laboratories reported 105 molecular diagnoses and 165 uncertain results in known and novel genes. Of these, clinicians interpreted 102 of 105 (97%) molecular diagnoses and 6 of 165 (4%) uncertain results as clinical-molecular diagnoses. The 108 clinical-molecular diagnoses were in 104 families (35% diagnostic yield). Each eligibility criteria resulted in diagnostic yields of 30% to 40%, and higher yields were achieved when >2 eligibility criteria were met (up to 45%). Hypothetical tests would have identified 61% of clinical-molecular diagnoses. CONCLUSION: We demonstrate robustness in eligibility criteria and high clinical validity of laboratory results from ES testing. The importance of ES was highlighted by the potential 40% of patients that would have gone undiagnosed without this test.

Bridging clinical care and research in Ontario, Canada: Maximizing diagnoses from reanalysis of clinical exome sequencing data.

We examined the utility of clinical and research processes in the reanalysis of publicly-funded clinical exome sequencing data in Ontario, Canada. In partnership with eight sites, we recruited 287 families with suspected rare genetic diseases tested between 2014 and 2020. Data from seven laboratories was reanalyzed with the referring clinicians. Reanalysis of clinically relevant genes identified diagnoses in 4% (13/287); four were missed by clinical testing. Translational research methods, including analysis of novel candidate genes, identified candidates in 21% (61/287). Of these, 24 families have additional evidence through data sharing to support likely diagnoses (8% of cohort). This study indicates few diagnoses are missed by clinical laboratories, the incremental gain from reanalysis of clinically-relevant genes is modest, and the highest yield comes from validation of novel disease-gene associations. Future implementation of translational research methods, including continued reporting of compelling genes of uncertain significance by clinical laboratories, should be considered to maximize diagnoses.

Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery.

Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R-SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data. Here, we introduce Genomics4RD, an integrated web-accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs. Genomics4RD has been designed to standardize data collection and processing, and to help users systematically collect, prioritize, and visualize participant information. Data storage, authorization, and access procedures have been developed in collaboration with policy experts and stakeholders to ensure the trusted and secure access of data by external researchers. The breadth and standardization of data offered by Genomics4RD allows researchers to compare candidate disease genes and variants between participants (i.e., matchmaking) for discovery purposes, while facilitating the development of computational approaches for multiomic data analyses and enabling clinical translation efforts for new genetic technologies in the future.

Landscape of DNA methylation on the X chromosome reflects CpG density, functional chromatin state and X-chromosome inactivation.

X-chromosome inactivation (XCI) achieves dosage compensation between males and females through the silencing of the majority of genes on one of the female X chromosomes. Thus, the female X chromosomes provide a unique opportunity to study euchromatin and heterochromatin of allelic regions within the same nuclear environment. We examined the interplay of DNA methylation (DNAm) with CpG density, transcriptional activity and chromatin state at genes on the X chromosome using over 1800 female samples analysed with the Illumina Infinium Human Methylation450 BeadChip. DNAm was used to predict an inactivation status for 63 novel transcription start sites (TSSs) across 27 tissues. There was high concordance of inactivation status across tissues, with 62% of TSSs subject to XCI in all 27 tissues examined, whereas 9% escaped from XCI in all tissues, and the remainder showed variable escape from XCI between females in subsets of tissues. Inter-female and twin data supported a model of predominately cis-acting influences on inactivation status. The level of expression from the inactive X relative to the active X correlated with the amount of female promoter DNAm to a threshold of ∼30%, beyond which genes were consistently subject to inactivation. The inactive X showed lower DNAm than the active X at intragenic and intergenic regions for genes subject to XCI, but not at genes that escape from inactivation. Our categorization of genes that escape from X inactivation provides candidates for sex-specific differences in disease.

Variant ATRX syndrome with dysfunction of ATRX and MAGT1 genes.

A 0.8 kb intronic duplication in MAGT1 and a single base pair deletion in the last exon of ATRX were identified using a chromosome X-specific microarray and exome sequencing in a family with five males demonstrating intellectual disability (ID) and unusual skin findings (e.g., generalized pruritus). MAGT1 is an Mg²âº transporter previously associated with primary immunodeficiency and ID, whereas mutations in ATRX cause ATRX-ID syndrome. In patient cells, the function of ATRX was demonstrated to be abnormal based on altered RNA/protein expression, hypomethylation of rDNA, and abnormal cytokinesis. Dysfunction of MAGT1 was reflected in reduced RNA/protein expression and Mg²âº influx. The mutation in ATRX most likely explains the ID, whereas MAGT1 disruption could be linked to abnormal skin findings, as normal magnesium homeostasis is necessary for skin health. This work supports observations that multiple mutations collectively contribute to the phenotypic variability of syndromic ID, and emphasizes the importance of correlating clinical phenotype with genomic and cell function analyses.

The application of epiphenotyping approaches to DNA methylation array studies of the human placenta.

Background : Genome-wide DNA methylation (DNAme) profiling of the placenta with Illumina Infinium Methylation bead arrays is often used to explore the connections between in utero exposures, placental pathology, and fetal development. However, many technical and biological factors can lead to signals of DNAme variation between samples and between cohorts, and understanding and accounting for these factors is essential to ensure meaningful and replicable data analysis. Recently, "epiphenotyping" approaches have been developed whereby DNAme data can be used to impute information about phenotypic variables such as gestational age, sex, cell composition, and ancestry. These epiphenotypes offer avenues to compare phenotypic data across cohorts, and to understand how phenotypic variables relate to DNAme variability. However, the relationships between placental epiphenotyping variables and other technical and biological variables, and their application to downstream epigenome analyses, have not been well studied. Results : Using DNAme data from 204 placentas across three cohorts, we applied the PlaNET R package to estimate epiphenotypes gestational age, ancestry, and cell composition in these samples. PlaNET ancestry estimates were highly correlated with independent polymorphic ancestry informative markers, and epigenetic gestational age, on average, was estimated within 4 days of reported gestational age, underscoring the accuracy of these tools. Cell composition estimates varied both within and between cohorts, but reassuringly were robust to placental processing time. Interestingly, the ratio of cytotrophoblast to syncytiotrophoblast proportion decreased with increasing gestational age, and differed slightly by both maternal ethnicity (lower in white vs. non-white) and genetic ancestry (lower in higher probability European ancestry). The cohort of origin and cytotrophoblast proportion were the largest drivers of DNAme variation in this dataset, based on their associations with the first principal component. Conclusions : This work confirms that cohort, array (technical) batch, cell type proportion, self-reported ethnicity, genetic ancestry, and biological sex are important variables to consider in any analyses of Illumina DNAme data. Further, we demonstrate that estimating epiphenotype variables from the DNAme data itself, when possible, provides both an independent check of clinically-obtained data and can provide a robust approach to compare variables across different datasets.

Profiling placental DNA methylation associated with maternal SSRI treatment during pregnancy.

Selective serotonin reuptake inhibitors (SSRIs) for treatment of prenatal maternal depression have been associated with neonatal neurobehavioral disturbances, though the molecular mechanisms remain poorly understood.  In utero exposure to SSRIs may affect DNA methylation (DNAme) in the human placenta, an epigenetic mark that is established during development and is associated with gene expression. Chorionic villus samples from 64 human placentas were profiled with the Illumina MethylationEPIC BeadChip; clinical assessments of maternal mood and SSRI treatment records were collected at multiple time points during pregnancy. Case distribution was 20 SSRI-exposed cases and 44 SSRI non-exposed cases. Maternal depression was defined using a mean maternal Hamilton Depression score > 8 to indicate symptomatic depressed mood ("maternally-depressed"), and we further classified cases into SSRI-exposed, maternally-depressed (n = 14); SSRI-exposed, not maternally-depressed (n = 6); SSRI non-exposed, maternally-depressed (n = 20); and SSRI non-exposed, not maternally-depressed (n = 24). For replication, Illumina 450K DNAme profiles were obtained from 34 additional cases from an independent cohort (n = 17 SSRI-exposed, n = 17 SSRI non-exposed). No CpGs were differentially methylated at FDR < 0.05 comparing SSRI-exposed to non-exposed placentas, in a model adjusted for mean maternal Hamilton Depression score, or in a model restricted to maternally-depressed cases with and without SSRI exposure. However, at a relaxed threshold of FDR < 0.25, five CpGs were differentially methylated (|Δß| > 0.03) by SSRI exposure status. Four were covered by the replication cohort measured by the 450K array, but none replicated. No CpGs were differentially methylated (FDR < 0.25) comparing maternally depressed to not depressed cases. In sex-stratified analyses for SSRI-exposed versus non-exposed cases (females n = 31; males n = 33), three additional CpGs in females, but none in males, were differentially methylated at the relaxed FDR < 0.25 cut-off. We did not observe large-scale alterations of DNAme in placentas exposed to maternal SSRI treatment, as compared to placentas with no SSRI exposure. We also found no evidence for altered DNAme in maternal depression-exposed versus depression non-exposed placentas. This novel work in a prospectively-recruited cohort with clinician-ascertained SSRI exposure and mood assessments would benefit from future replication.

Comparing genome sequencing technologies to improve rare disease diagnostics: a protocol for the evaluation of a pilot project, Genome-wide Sequencing Ontario.

BACKGROUND: Genome-wide sequencing has emerged as a promising strategy for the timely diagnosis of rare diseases, but it is not yet available as a clinical test performed in Canadian diagnostic laboratories. We describe the protocol for evaluating a 2-year pilot project, Genome-wide Sequencing Ontario, to offer high-quality clinical genome-wide sequencing in Ontario, Canada. METHODS: The Genome-wide Sequencing Ontario protocol was codesigned by the Ontario Ministry of Health, the Hospital for Sick Children in Toronto and the Children's Hospital of Eastern Ontario in Ottawa. Enrolment of a prospective cohort of patients began on Apr. 1, 2021. Eligible cases with blood samples available for the index case and both parents (i.e., trios) are randomized to receive exome sequencing or genome sequencing. We will collect patient-level data and ascertain costs associated with the laboratory workflow for exome sequencing and genome sequencing. We will compare point estimates for the diagnostic utility and timeliness of exome sequencing and genome sequencing, and we will determine an incremental cost-effectiveness ratio (expressed as the incremental cost of genome sequencing versus exome sequencing per additional patient with a causal variant detected). INTERPRETATION: Findings from this work will provide robust evidence for the diagnostic utility, cost-effectiveness and timeliness of exome sequencing and genome sequencing, and will be disseminated via academic publications and policy briefs. Findings will inform provincial and cross-provincial policy related to the long-term organization, delivery and reimbursement of clinical-grade genome diagnostics for rare disease.

Human placental piwi-interacting RNA transcriptome is characterized by expression from the DLK1-DIO3 imprinted region.

The placenta is vital to embryonic development and requires a finely-tuned pattern of gene expression, achieved in part by its unique epigenetic landscape. Piwi-interacting RNAs (piRNAs) are a class of small-non-coding RNA with established roles as epigenetic regulators of gene expression, largely via methylation of targeted DNA sequences. The expression of piRNAs have mainly been described in germ cells, but a fraction have been shown to retain expression in adult somatic tissues. To aid in understanding the contribution of these regulators in the placenta, we provide the first description of the piRNA transcriptome in human placentas. We find 297 piRNAs to be preferentially expressed in the human placenta, a subset of which are expressed at higher levels relative to testes samples. We also observed a large proportion of placental piRNAs to be expressed from a single locus, as distinct from canonical cluster locations associated with transposable element silencing. Finally, we find that 15 of the highest-expressed placental piRNAs maps to the DLK1-DIO3 locus, suggesting a link to placental biology. Our findings suggest that piRNAs could contribute to the molecular networks defining placental function in humans, and a biological impact of piRNA expression beyond germ cells.

Profiling the small non-coding RNA transcriptome of the human placenta.

Proper functioning of the human placenta is critical for maternal and fetal health. While microRNAs (miRNAs) are known to impact placental gene expression, the effects of other small non-coding RNAs (sncRNAs) on the placental transcriptome are not well-established, and are emerging topics in the study of environmental influence on fetal development and reproductive health. Here, we assembled a cohort of 30 placental chorionic villi samples of varying gestational ages (M ± SD = 23.7 ± 11.3 weeks) to delineate the human placental sncRNA transcriptome through small RNA sequence analysis. We observed expression of 1544 sncRNAs, which include 48 miRNAs previously unannotated in humans. Additionally, 18,003 miRNA variants (isomiRs) were identified from the 654 observed miRNA species. This characterization of the term and pre-term placental sncRNA transcriptomes provides data fundamental to future investigations of their regulatory functions in the human placenta, and the baseline expression pattern needed for identifying changes in response to environmental factors, or under disease conditions.