Electronic health record signatures identify undiagnosed patients with common variable immunodeficiency disease.

Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases and uses this knowledge to rank patients by likelihood of having CVID. PheNet could have diagnosed more than half of our patients with CVID 1 or more years earlier than they had been diagnosed. When applied to a large EHR dataset, followed by blinded chart review of the top 100 patients ranked by PheNet, we found that 74% were highly probable to have CVID. We externally validated PheNet using >6 million records from disparate medical systems in California and Tennessee. As artificial intelligence and machine learning make their way into health care, we show that algorithms such as PheNet can offer clinical benefits by expediting the diagnosis of rare diseases.

KAT6A mutations in Arboleda-Tham syndrome drive epigenetic regulation of posterior HOXC cluster.

Arboleda-Tham Syndrome (ARTHS) is a rare genetic disorder caused by heterozygous, de novo mutations in Lysine(K) acetyltransferase 6A (KAT6A). ARTHS is clinically heterogeneous and characterized by several common features, including intellectual disability, developmental and speech delay, and hypotonia, and affects multiple organ systems. KAT6A is the enzymatic core of a histone-acetylation protein complex; however, the direct histone targets and gene regulatory effects remain unknown. In this study, we use ARTHS patient (n = 8) and control (n = 14) dermal fibroblasts and perform comprehensive profiling of the epigenome and transcriptome caused by KAT6A mutations. We identified differential chromatin accessibility within the promoter or gene body of 23% (14/60) of genes that were differentially expressed between ARTHS and controls. Within fibroblasts, we show a distinct set of genes from the posterior HOXC gene cluster (HOXC10, HOXC11, HOXC-AS3, HOXC-AS2, and HOTAIR) that are overexpressed in ARTHS and are transcription factors critical for early development body segment patterning. The genomic loci harboring HOXC genes are epigenetically regulated with increased chromatin accessibility, high levels of H3K23ac, and increased gene-body DNA methylation compared to controls, all of which are consistent with transcriptomic overexpression. Finally, we used unbiased proteomic mass spectrometry and identified two new histone post-translational modifications (PTMs) that are disrupted in ARTHS: H2A and H3K56 acetylation. Our multi-omics assays have identified novel histone and gene regulatory roles of KAT6A in a large group of ARTHS patients harboring diverse pathogenic mutations. This work provides insight into the role of KAT6A on the epigenomic regulation in somatic cell types.

KAT6A mutations in Arboleda-Tham syndrome drive epigenetic regulation of posterior HOXC cluster.

Arboleda-Tham Syndrome (ARTHS) is a rare genetic disorder caused by heterozygous, de novo truncating mutations in Lysine(K) acetyltransferase 6A (KAT6A). ARTHS is clinically heterogeneous and characterized by several common features including intellectual disability, developmental and speech delay, hypotonia and affects multiple organ systems. KAT6A is highly expressed in early development and plays a key role in cell-type specific differentiation. KAT6A is the enzymatic core of a histone-acetylation protein complex, however the direct histone targets and gene regulatory effects remain unknown. In this study, we use ARTHS patient (n=8) and control (n=14) dermal fibroblasts and perform comprehensive profiling of the epigenome and transcriptome caused by KAT6A mutations. We identified differential chromatin accessibility within the promoter or gene body of 23%(14/60) of genes that were differentially expressed between ARTHS and controls. Within fibroblasts, we show a distinct set of genes from the posterior HOXC gene cluster (HOXC10, HOXC11, HOXC-AS3, HOXC-AS2, HOTAIR) that are overexpressed in ARTHS and are transcription factors critical for early development body segment patterning. The genomic loci harboring HOXC genes are epigenetically regulated with increased chromatin accessibility, high levels of H3K23ac, and increased gene-body DNA methylation compared to controls, all of which are consistent with transcriptomic overexpression. Finally, we used unbiased proteomic mass spectrometry and identified two new histone post-translational modifications (PTMs) that are disrupted in ARTHS: H2A and H3K56 acetylation. Our multi-omics assays have identified novel histone and gene regulatory roles of KAT6A in a large group of ARTHS patients harboring diverse pathogenic mutations. This work provides insight into the role of KAT6A on the epigenomic regulation in somatic cell types.

DMRscaler: a scale-aware method to identify regions of differential DNA methylation spanning basepair to multi-megabase features.

BACKGROUND: Pathogenic mutations in genes that control chromatin function have been implicated in rare genetic syndromes. These chromatin modifiers exhibit extraordinary diversity in the scale of the epigenetic changes they affect, from single basepair modifications by DNMT1 to whole genome structural changes by PRM1/2. Patterns of DNA methylation are related to a diverse set of epigenetic features across this full range of epigenetic scale, making DNA methylation valuable for mapping regions of general epigenetic dysregulation. However, existing methods are unable to accurately identify regions of differential methylation across this full range of epigenetic scale directly from DNA methylation data. RESULTS: To address this, we developed DMRscaler, a novel method that uses an iterative windowing procedure to capture regions of differential DNA methylation (DMRs) ranging in size from single basepairs to whole chromosomes. We benchmarked DMRscaler against several DMR callers in simulated and natural data comparing XX and XY peripheral blood samples. DMRscaler was the only method that accurately called DMRs ranging in size from 100 bp to 1 Mb (pearson's r = 0.94) and up to 152 Mb on the X-chromosome. We then analyzed methylation data from rare-disease cohorts that harbor chromatin modifier gene mutations in NSD1, EZH2, and KAT6A where DMRscaler identified novel DMRs spanning gene clusters involved in development. CONCLUSION: Taken together, our results show DMRscaler is uniquely able to capture the size of DMR features across the full range of epigenetic scale and identify novel, co-regulated regions that drive epigenetic dysregulation in human disease.

Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity.

The spatial and temporal domain of a gene's expression can range from ubiquitous to highly specific. Quantifying the degree to which this expression is unique to a specific tissue or developmental timepoint can provide insight into the etiology of genetic diseases. However, quantifying specificity remains challenging as measures of specificity are sensitive to similarity between samples in the sample set. For example, in the Gene-Tissue Expression project (GTEx), brain subregions are overrepresented at 13 of 54 (24%) unique tissues sampled. In this dataset, existing specificity measures have a decreased ability to identify genes specific to the brain relative to other organs. To solve this problem, we leverage sample similarity information to weight samples such that overrepresented tissues do not have an outsized effect on specificity estimates. We test this reweighting procedure on 4 measures of specificity, Z-score, Tau, Tsi and Gini, in the GTEx data and in single cell datasets for zebrafish and mouse. For all of these measures, incorporating sample similarity information to weight samples results in greater stability of sets of genes called as specific and decreases the overall variance in the change of specificity estimates as sample sets become more unbalanced. Furthermore, the genes with the largest improvement in their specificity estimate's stability are those with functions related to the overrepresented sample types. Our results demonstrate that incorporating similarity information improves specificity estimates' stability to the choice of the sample set used to define the transcriptome, providing more robust and reproducible measures of specificity for downstream analyses.

Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart disease.

Congenital heart disease (CHD) is a rare structural defect that occurs in ∼1% of live births. Studies on CHD genetic architecture have identified pathogenic single-gene mutations in less than 30% of cases. Single-gene mutations often show incomplete penetrance and variable expressivity. Therefore, we hypothesize that genetic background may play a role in modulating disease expression. Polygenic risk scores (PRSs) aggregate effects of common genetic variants to investigate whether, cumulatively, these variants are associated with disease penetrance or severity. However, the major limitations in this field have been in generating sufficient sample sizes for these studies. Here we used CHD-phenotype matched genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) as our base study and whole-genome sequencing data from the CHD cohort (n1 = 711 trios, n2 = 362 European trios) of the Gabriella Miller Kids First dataset as our target study to develop PRSs for CHD. PRSs estimated using a GWAS for heart valve problems and heart murmur explain 2.5% of the variance in case-control status of CHD (all SNVs, p = 7.90 × 10-3; fetal cardiac SNVs, p = 8.00 × 10-3) and 1.8% of the variance in severity of CHD (fetal cardiac SNVs, p = 6.20 × 10-3; all SNVs, p = 0.015). These results show that common variants captured in CHD phenotype-matched GWASs have a modest but significant contribution to phenotypic expression of CHD. Further exploration of the cumulative effect of common variants is necessary for understanding the complex genetic etiology of CHD and other rare diseases.