Insurance denials and diagnostic rates in a pediatric genomic research cohort.

PURPOSE: This study aimed to assess the amount and types of clinical genetic testing denied by insurance and the rate of diagnostic and candidate genetic findings identified through research in patients who faced insurance denials. METHODS: Analysis consisted of review of insurance denials in 801 patients enrolled in a pediatric genomic research repository with either no previous genetic testing or previous negative genetic testing result identified through cross-referencing with insurance prior-authorizations in patient medical records. Patients and denials were also categorized by type of insurance coverage. Diagnostic findings and candidate genetic findings in these groups were determined through review of our internal variant database and patient charts. RESULTS: Of the 801 patients analyzed, 147 had insurance prior-authorization denials on record (18.3%). Exome sequencing and microarray were the most frequently denied genetic tests. Private insurance was significantly more likely to deny testing than public insurance (odds ratio = 2.03 [95% CI = 1.38-2.99] P = .0003). Of the 147 patients with insurance denials, 53.7% had at least 1 diagnostic or candidate finding and 10.9% specifically had a clinically diagnostic finding. Fifty percent of patients with clinically diagnostic results had immediate medical management changes (5.4% of all patients experiencing denials). CONCLUSION: Many patients face a major barrier to genetic testing in the form of lack of insurance coverage. A number of these patients have clinically diagnostic findings with medical management implications that would not have been identified without access to research testing. These findings support re-evaluation of insurance carriers' coverage policies.

Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes.

PURPOSE: This study aimed to provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids program. METHODS: Extensive analyses of 960 families with suspected genetic disorders included short-read exome sequencing and short-read genome sequencing (srGS); PacBio HiFi long-read genome sequencing (HiFi-GS); variant calling for single nucleotide variants (SNV), structural variant (SV), and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants, and pedigrees were stored in PhenoTips database, with data sharing through controlled access the database of Genotypes and Phenotypes. RESULTS: Diagnostic rates ranged from 11% in patients with prior negative genetic testing to 34.5% in naive patients. Incorporating SVs from genome sequencing added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs compared with srGS. Variants and genes of unknown significance remain the most common finding (58% of nondiagnostic cases). CONCLUSION: Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated using HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation and by providing HiFi variant (SNV/SV) resources from >1000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.

Mapping structural variants to rare disease genes using long-read whole genome sequencing and trait-relevant polygenic scores.

Recent studies have revealed the pervasive landscape of rare structural variants (rSVs) present in human genomes. rSVs can have extreme effects on the expression of proximal genes and, in a rare disease context, have been implicated in patient cases where no diagnostic single nucleotide variant (SNV) was found. Approaches for integrating rSVs to date have focused on targeted approaches in known Mendelian rare disease genes. This approach is intractable for rare diseases with many causal loci or patients with complex, multi-phenotype syndromes. We hypothesized that integrating trait-relevant polygenic scores (PGS) would provide a substantial reduction in the number of candidate disease genes in which to assess rSV effects. We further implemented a method for ranking PGS genes to define a set of core/key genes where a rSV has the potential to exert relatively larger effects on disease risk. Among a subset of patients enrolled in the Genomic Answers for Kids (GA4K) rare disease program (N=497), we used PacBio HiFi long-read whole genome sequencing (lrWGS) to identify rSVs intersecting genes in trait-relevant PGSs. Illustrating our approach in Autism (N=54 cases), we identified 22, 019 deletions, 2,041 duplications, 87,826 insertions, and 214 inversions overlapping putative core/key PGS genes. Additionally, by integrating genomic constraint annotations from gnomAD, we observed that rare duplications overlapping putative core/key PGS genes were frequently in higher constraint regions compared to controls (P = 1×10-03). This difference was not observed in the lowest-ranked gene set (P = 0.15). Overall, our study provides a framework for the annotation of long-read rSVs from lrWGS data and prioritization of disease-linked genomic regions for downstream functional validation of rSV impacts. To enable reuse by other researchers, we have made SV allele frequencies and gene associations freely available.

Pangenome graphs improve the analysis of structural variants in rare genetic diseases.

Rare DNA alterations that cause heritable diseases are only partially resolvable by clinical next-generation sequencing due to the difficulty of detecting structural variation (SV) in all genomic contexts. Long-read, high fidelity genome sequencing (HiFi-GS) detects SVs with increased sensitivity and enables assembling personal and graph genomes. We leverage standard reference genomes, public assemblies (n = 94) and a large collection of HiFi-GS data from a rare disease program (Genomic Answers for Kids, GA4K, n = 574 assemblies) to build a graph genome representing a unified SV callset in GA4K, identify common variation and prioritize SVs that are more likely to cause genetic disease (MAF < 0.01). Using graphs, we obtain a higher level of reproducibility than the standard reference approach. We observe over 200,000 SV alleles unique to GA4K, including nearly 1000 rare variants that impact coding sequence. With improved specificity for rare SVs, we isolate 30 candidate SVs in phenotypically prioritized genes, including known disease SVs. We isolate a novel diagnostic SV in KMT2E, demonstrating use of personal assemblies coupled with pangenome graphs for rare disease genomics. The community may interrogate our pangenome with additional assemblies to discover new SVs within the allele frequency spectrum relevant to genetic diseases.

Characterization and visualization of tandem repeats at genome scale.

Tandem repeat (TR) variation is associated with gene expression changes and numerous rare monogenic diseases. Although long-read sequencing provides accurate full-length sequences and methylation of TRs, there is still a need for computational methods to profile TRs across the genome. Here we introduce the Tandem Repeat Genotyping Tool (TRGT) and an accompanying TR database. TRGT determines the consensus sequences and methylation levels of specified TRs from PacBio HiFi sequencing data. It also reports reads that support each repeat allele. These reads can be subsequently visualized with a companion TR visualization tool. Assessing 937,122 TRs, TRGT showed a Mendelian concordance of 98.38%, allowing a single repeat unit difference. In six samples with known repeat expansions, TRGT detected all expansions while also identifying methylation signals and mosaicism and providing finer repeat length resolution than existing methods. Additionally, we released a database with allele sequences and methylation levels for 937,122 TRs across 100 genomes.

A common flanking variant is associated with enhanced stability of the FGF14-SCA27B repeat locus.

The factors driving or preventing pathological expansion of tandem repeats remain largely unknown. Here, we assessed the FGF14 (GAA)·(TTC) repeat locus in 2,530 individuals by long-read and Sanger sequencing and identified a common 5'-flanking variant in 70.34% of alleles analyzed (3,463/4,923) that represents the phylogenetically ancestral allele and is present on all major haplotypes. This common sequence variation is present nearly exclusively on nonpathogenic alleles with fewer than 30 GAA-pure triplets and is associated with enhanced stability of the repeat locus upon intergenerational transmission and increased Fiber-seq chromatin accessibility.

Adverse Maternal Environments Perturb Hepatic DNA Methylome and Transcriptome Prior to the Adult-Onset Non-Alcoholic Fatty Liver Disease in Mouse Offspring.

Exposure to adverse early-life environments (AME) increases the incidence of developing adult-onset non-alcoholic fatty liver disease (NAFLD). DNA methylation has been postulated to link AME and late-onset diseases. This study aimed to investigate whether and to what extent the hepatic DNA methylome was perturbed prior to the development of NAFLD in offspring exposed to AME in mice. AME constituted maternal Western diet and late-gestational stress. Male offspring livers at birth (d0) and weaning (d21) were used for evaluating the DNA methylome and transcriptome using the reduced representation of bisulfite sequencing and RNA-seq, respectively. We found AME caused 5879 differentially methylated regions (DMRs) and zero differentially expressed genes (DEGs) at d0 and 2970 and 123, respectively, at d21. The majority of the DMRs were distal to gene transcription start sites and did not correlate with DEGs. The DEGs at d21 were significantly enriched in GO biological processes characteristic of liver metabolic functions. In conclusion, AME drove changes in the hepatic DNA methylome, which preceded perturbations in the hepatic metabolic transcriptome, which preceded the onset of NAFLD. We speculate that subtle impacts on dynamic enhancers lead to long-range regulatory changes that manifest over time as gene network alternations and increase the incidence of NAFLD later in life.

Extravillous trophoblast cell lineage development is associated with active remodeling of the chromatin landscape.

The extravillous trophoblast cell lineage is a key feature of placentation and successful pregnancy. Knowledge of transcriptional regulation driving extravillous trophoblast cell development is limited. Here, we map the transcriptome and epigenome landscape as well as chromatin interactions of human trophoblast stem cells and their transition into extravillous trophoblast cells. We show that integrating chromatin accessibility, long-range chromatin interactions, transcriptomic, and transcription factor binding motif enrichment enables identification of transcription factors and regulatory mechanisms critical for extravillous trophoblast cell development. We elucidate functional roles for TFAP2C, SNAI1, and EPAS1 in the regulation of extravillous trophoblast cell development. EPAS1 is identified as an upstream regulator of key extravillous trophoblast cell transcription factors, including ASCL2 and SNAI1 and together with its target genes, is linked to pregnancy loss and birth weight. Collectively, we reveal activation of a dynamic regulatory network and provide a framework for understanding extravillous trophoblast cell specification in trophoblast cell lineage development and human placentation.

Direct haplotype-resolved 5-base HiFi sequencing for genome-wide profiling of hypermethylation outliers in a rare disease cohort.

Long-read HiFi genome sequencing allows for accurate detection and direct phasing of single nucleotide variants, indels, and structural variants. Recent algorithmic development enables simultaneous detection of CpG methylation for analysis of regulatory element activity directly in HiFi reads. We present a comprehensive haplotype resolved 5-base HiFi genome sequencing dataset from a rare disease cohort of 276 samples in 152 families to identify rare (~0.5%) hypermethylation events. We find that 80% of these events are allele-specific and predicted to cause loss of regulatory element activity. We demonstrate heritability of extreme hypermethylation including rare cis variants associated with short (~200 bp) and large hypermethylation events (>1 kb), respectively. We identify repeat expansions in proximal promoters predicting allelic gene silencing via hypermethylation and demonstrate allelic transcriptional events downstream. On average 30-40 rare hypermethylation tiles overlap rare disease genes per patient, providing indications for variation prioritization including a previously undiagnosed pathogenic allele in DIP2B causing global developmental delay. We propose that use of HiFi genome sequencing in unsolved rare disease cases will allow detection of unconventional diseases alleles due to loss of regulatory element activity.

Quantitative biomedical annotation using medical subject heading over-representation profiles (MeSHOPs).

BACKGROUND: MEDLINE®/PubMed® indexes over 20 million biomedical articles, providing curated annotation of its contents using a controlled vocabulary known as Medical Subject Headings (MeSH). The MeSH vocabulary, developed over 50+ years, provides a broad coverage of topics across biomedical research. Distilling the essential biomedical themes for a topic of interest from the relevant literature is important to both understand the importance of related concepts and discover new relationships. RESULTS: We introduce a novel method for determining enriched curator-assigned MeSH annotations in a set of papers associated to a topic, such as a gene, an author or a disease. We generate MeSH Over-representation Profiles (MeSHOPs) to quantitatively summarize the annotations in a form convenient for further computational analysis and visualization. Based on a hypergeometric distribution of assigned terms, MeSHOPs statistically account for the prevalence of the associated biomedical annotation while highlighting unusually prevalent terms based on a specified background. MeSHOPs can be visualized using word clouds, providing a succinct quantitative graphical representation of the relative importance of terms. Using the publication dates of articles, MeSHOPs track changing patterns of annotation over time. Since MeSHOPs are quantitative vectors, MeSHOPs can be compared using standard techniques such as hierarchical clustering. The reliability of MeSHOP annotations is assessed based on the capacity to re-derive the subset of the Gene Ontology annotations with equivalent MeSH terms. CONCLUSIONS: MeSHOPs allows quantitative measurement of the degree of association between any entity and the annotated medical concepts, based directly on relevant primary literature. Comparison of MeSHOPs allows entities to be related based on shared medical themes in their literature. A web interface is provided for generating and visualizing MeSHOPs.

Immune cell residency in the nasal mucosa may partially explain respiratory disease severity across the age range.

Previous studies focusing on the age disparity in COVID-19 severity have suggested that younger individuals mount a more robust innate immune response in the nasal mucosa after infection with SARS-CoV-2. However, it is unclear if this reflects increased immune activation or increased immune residence in the nasal mucosa. We hypothesized that immune residency in the nasal mucosa of healthy individuals may differ across the age range. We applied single-cell RNA-sequencing and measured the cellular composition and transcriptional profile of the nasal mucosa in 35 SARS-CoV-2 negative children and adults, ranging in age from 4 months to 65 years. We analyzed in total of ~ 30,000 immune and epithelial cells and found that age and immune cell proportion in the nasal mucosa are inversely correlated, with little evidence for structural changes in the transcriptional state of a given cell type across the age range. Orthogonal validation by epigenome sequencing indicate that it is especially cells of the innate immune system that underlie the age-association. Additionally, we characterize the predominate immune cell type in the nasal mucosa: a resident T cell like population with potent antiviral properties. These results demonstrate fundamental changes in the immune cell makeup of the uninfected nasal mucosa over the lifespan. The resource we generate here is an asset for future studies focusing on respiratory infection and immunization strategies.

Single-cell analysis of human adipose tissue identifies depot and disease specific cell types.

The complex relationship between metabolic disease risk and body fat distribution in humans involves cellular characteristics which are specific to body fat compartments. Here we show depot-specific differences in the stromal vascual fraction of visceral and subcutaneous adipose tissue by performing single-cell RNA sequencing of tissue specimen from obese individuals. We characterize multiple immune cells, endothelial cells, fibroblasts, adipose and hematopoietic stem cell progenitors. Subpopulations of adipose-resident immune cells are metabolically active and associated with metabolic disease status and those include a population of potential dysfunctional CD8+ T cells expressing metallothioneins. We identify multiple types of adipocyte progenitors that are common across depots, including a subtype enriched in individuals with type 2 diabetes. Depot-specific analysis reveals a class of adipocyte progenitors unique to visceral adipose tissue, which shares common features with beige preadipocytes. Our human single-cell transcriptome atlas across fat depots provides a resource to dissect functional genomics of metabolic disease.

Dissecting features of epigenetic variants underlying cardiometabolic risk using full-resolution epigenome profiling in regulatory elements.

Sparse profiling of CpG methylation in blood by microarrays has identified epigenetic links to common diseases. Here we apply methylC-capture sequencing (MCC-Seq) in a clinical population of ~200 adipose tissue and matched blood samples (Ntotal~400), providing high-resolution methylation profiling (>1.3 M CpGs) at regulatory elements. We link methylation to cardiometabolic risk through associations to circulating plasma lipid levels and identify lipid-associated CpGs with unique localization patterns in regulatory elements. We show distinct features of tissue-specific versus tissue-independent lipid-linked regulatory regions by contrasting with parallel assessments in ~800 independent adipose tissue and blood samples from the general population. We follow-up on adipose-specific regulatory regions under (1) genetic and (2) epigenetic (environmental) regulation via integrational studies. Overall, the comprehensive sequencing of regulatory element methylomes reveals a rich landscape of functional variants linked genetically as well as epigenetically to plasma lipid traits.

H3K27M induces defective chromatin spread of PRC2-mediated repressive H3K27me2/me3 and is essential for glioma tumorigenesis.

Lys-27-Met mutations in histone 3 genes (H3K27M) characterize a subgroup of deadly gliomas and decrease genome-wide H3K27 trimethylation. Here we use primary H3K27M tumor lines and isogenic CRISPR-edited controls to assess H3K27M effects in vitro and in vivo. We find that whereas H3K27me3 and H3K27me2 are normally deposited by PRC2 across broad regions, their deposition is severely reduced in H3.3K27M cells. H3K27me3 is unable to spread from large unmethylated CpG islands, while H3K27me2 can be deposited outside these PRC2 high-affinity sites but to levels corresponding to H3K27me3 deposition in wild-type cells. Our findings indicate that PRC2 recruitment and propagation on chromatin are seemingly unaffected by K27M, which mostly impairs spread of the repressive marks it catalyzes, especially H3K27me3. Genome-wide loss of H3K27me3 and me2 deposition has limited transcriptomic consequences, preferentially affecting lowly-expressed genes regulating neurogenesis. Removal of H3K27M restores H3K27me2/me3 spread, impairs cell proliferation, and completely abolishes their capacity to form tumors in mice.

Correction to: Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome.

Following publication of the original article [1], the authors reported an error in Additional file 1.

Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome.

BACKGROUND: The functional impact of genetic variation has been extensively surveyed, revealing that genetic changes correlated to phenotypes lie mostly in non-coding genomic regions. Studies have linked allele-specific genetic changes to gene expression, DNA methylation, and histone marks but these investigations have only been carried out in a limited set of samples. RESULTS: We describe a large-scale coordinated study of allelic and non-allelic effects on DNA methylation, histone mark deposition, and gene expression, detecting the interrelations between epigenetic and functional features at unprecedented resolution. We use information from whole genome and targeted bisulfite sequencing from 910 samples to perform genotype-dependent analyses of allele-specific methylation (ASM) and non-allelic methylation (mQTL). In addition, we introduce a novel genotype-independent test to detect methylation imbalance between chromosomes. Of the ~2.2 million CpGs tested for ASM, mQTL, and genotype-independent effects, we identify ~32% as being genetically regulated (ASM or mQTL) and ~14% as being putatively epigenetically regulated. We also show that epigenetically driven effects are strongly enriched in repressed regions and near transcription start sites, whereas the genetically regulated CpGs are enriched in enhancers. Known imprinted regions are enriched among epigenetically regulated loci, but we also observe several novel genomic regions (e.g., HOX genes) as being epigenetically regulated. Finally, we use our ASM datasets for functional interpretation of disease-associated loci and show the advantage of utilizing naïve T cells for understanding autoimmune diseases. CONCLUSIONS: Our rich catalogue of haploid methylomes across multiple tissues will allow validation of epigenome association studies and exploration of new biological models for allelic exclusion in the human genome.

Increased DNA methylation variability in type 1 diabetes across three immune effector cell types.

The incidence of type 1 diabetes (T1D) has substantially increased over the past decade, suggesting a role for non-genetic factors such as epigenetic mechanisms in disease development. Here we present an epigenome-wide association study across 406,365 CpGs in 52 monozygotic twin pairs discordant for T1D in three immune effector cell types. We observe a substantial enrichment of differentially variable CpG positions (DVPs) in T1D twins when compared with their healthy co-twins and when compared with healthy, unrelated individuals. These T1D-associated DVPs are found to be temporally stable and enriched at gene regulatory elements. Integration with cell type-specific gene regulatory circuits highlight pathways involved in immune cell metabolism and the cell cycle, including mTOR signalling. Evidence from cord blood of newborns who progress to overt T1D suggests that the DVPs likely emerge after birth. Our findings, based on 772 methylomes, implicate epigenetic changes that could contribute to disease pathogenesis in T1D.

Risk of re-identification of epigenetic methylation data: a more nuanced response is needed.

In this letter to the editor, we respond to the recent publication by Philibert et al. Methylation array data can simultaneously identify individuals and convey protected health information: an unrecognized ethical concern (Clinical Epigenetics 2014, 6:28). Further discussion of the issues raised by the risk of re-identification of epigenetic methylation data is needed, and a more nuanced approach should be taken with respect to its implications for data sharing policy than the one provided.

Population whole-genome bisulfite sequencing across two tissues highlights the environment as the principal source of human methylome variation.

BACKGROUND: CpG methylation variation is involved in human trait formation and disease susceptibility. Analyses within populations have been biased towards CpG-dense regions through the application of targeted arrays. We generate whole-genome bisulfite sequencing data for approximately 30 adipose and blood samples from monozygotic and dizygotic twins for the characterization of non-genetic and genetic effects at single-site resolution. RESULTS: Purely invariable CpGs display a bimodal distribution with enrichment of unmethylated CpGs and depletion of fully methylated CpGs in promoter and enhancer regions. Population-variable CpGs account for approximately 15-20 % of total CpGs per tissue, are enriched in enhancer-associated regions and depleted in promoters, and single nucleotide polymorphisms at CpGs are a frequent confounder of extreme methylation variation. Differential methylation is primarily non-genetic in origin, with non-shared environment accounting for most of the variance. These non-genetic effects are mainly tissue-specific. Tobacco smoking is associated with differential methylation in blood with no evidence of this exposure impacting cell counts. Opposite to non-genetic effects, genetic effects of CpG methylation are shared across tissues and thus limit inter-tissue epigenetic drift. CpH methylation is rare, and shows similar characteristics of variation patterns as CpGs. CONCLUSIONS: Our study highlights the utility of low pass whole-genome bisulfite sequencing in identifying methylome variation beyond promoter regions, and suggests that targeting the population dynamic methylome of tissues requires assessment of understudied intergenic CpGs distal to gene promoters to reveal the full extent of inter-individual variation.