Phenotype Ontology Terms Research Articles

Biliary atresia is associated with polygenic susceptibility in ciliogenesis and planar polarity effector genes

Biliary atresia (BA) is poorly understood and leads to liver transplantation (LT), with the requirement for and associated risks of lifelong immunosuppression, in most children. We performed a genome-wide association study (GWAS) to determine the genetic basis of BA. We performed a GWAS in 811 European BA cases treated with LT in US, Canadian and UK centers, and 4,654 genetically matched controls. Whole-genome sequencing of 100 cases evaluated synthetic association with rare variants. Functional studies included whole liver transcriptome analysis of 64 BA cases and perturbations in experimental models. A GWAS of common single nucleotide polymorphisms (SNPs), i.e. allele frequencies >1%, identified intronic SNPs rs6446628 in AFAP1 with genome-wide significance (p= 3.93E-8) and rs34599046 in TUSC3 at sub-threshold genome-wide significance (p= 1.34E-7), both supported by credible peaks of neighboring SNPs. Like other previously reported BA-associated genes, AFAP1 and TUSC3 are ciliogenesis and planar polarity effectors (CPLANE). In gene-set-based GWAS, BA was associated with 6,005 SNPs in 102 CPLANE genes (p= 5.84E-15). Compared with non-CPLANE genes, more CPLANE genes harbored rare variants (allele frequency <1%) that were assigned Human Phenotype Ontology terms related to hepatobiliary anomalies by predictive algorithms, 87% vs. 40%, p <0.0001. Rare variants were present in multiple genes distinct from those with BA-associated common variants in most BA cases. AFAP1 and TUSC3 knockdown blocked ciliogenesis in mouse tracheal cells. Inhibition of ciliogenesis caused biliary dysgenesis in zebrafish. AFAP1 and TUSC3 were expressed in fetal liver organoids, as well as fetal and BA livers, but not in normal or disease-control livers. Integrative analysis of BA-associated variants and liver transcripts revealed abnormal vasculogenesis and epithelial tube formation, explaining portal vein anomalies that co-exist with BA. BA is associated with polygenic susceptibility in CPLANE genes. Rare variants contribute to polygenic risk in vulnerable pathways via unique genes. Liver transplantation is needed to cure most children born with biliary atresia, a poorly understood rare disease. Transplant immunosuppression increases the likelihood of life-threatening infections and cancers. To improve care by preventing this disease and its progression to transplantation, we examined its genetic basis. We find that this disease is associated with both common and rare mutations in highly specialized genes which maintain normal communication and movement of cells, and their organization into bile ducts and blood vessels during early development of the human embryo. Because defects in these genes also cause other birth defects, our findings could lead to preventive strategies to lower the incidence of biliary atresia and potentially other birth defects.

Integration of Phenotype Term Prioritization and Gene Expression Analysis Reveals a Novel Variant in the PERP Gene Associated with Autosomal Recessive Erythrokeratoderma

Hereditary palmoplantar keratodermas (PPKs) are a clinically and genetically heterogeneous group of disorders characterized by excessive epidermal thickening of palms and soles. Several genes have been associated with PPK including PERP, a gene encoding a crucial component of desmosomes that has been associated with dominant and recessive keratoderma. We report a patient with recessive erythrokeratoderma (EK) in which whole exome sequencing (WES) prioritized by human phenotype ontology (HPO) terms revealed the presence of the novel variant c.153C &gt; A in the N-terminal region the PERP gene. This variant is predicted to have a nonsense effect, p.(Cys51Ter), resulting in a premature stop codon. We demonstrated a marked reduction in gene expression in cultured skin fibroblasts obtained from the patient. Despite the PERP gene is expressed at low levels in fibroblasts, our finding supports a loss-of-function (LoF) mechanism for the identified variant, as previously suggested in recessive EK. Our study underscores the importance of integrating HPO analysis when using WES for molecular genetic diagnosis in a clinical setting, as it facilitates continuous updates regarding gene–clinical feature associations.

At a glance: the largest Niemann-Pick type C1 cohort with 602 patients diagnosed over 15 years

Niemann-Pick type C1 disease (NPC1 [OMIM 257220]) is a rare and severe autosomal recessive disorder, characterized by a multitude of neurovisceral clinical manifestations and a fatal outcome with no effective treatment to date. Aiming to gain insights into the genetic aspects of the disease, clinical, genetic, and biomarker PPCS data from 602 patients referred from 47 countries and diagnosed with NPC1 in our laboratory were analyzed. Patients’ clinical data were dissected using Human Phenotype Ontology (HPO) terms, and genotype–phenotype analysis was performed. The median age at diagnosis was 10.6 years (range 0–64.5 years), with 287 unique pathogenic/likely pathogenic (P/LP) variants identified, expanding NPC1 allelic heterogeneity. Importantly, 73 P/LP variants were previously unpublished. The most frequent variants detected were: c.3019C > G, p.(P1007A), c.3104C > T, p.(A1035V), and c.2861C > T, p.(S954L). Loss of function (LoF) variants were significantly associated with earlier age at diagnosis, highly increased biomarker levels, and a visceral phenotype (abnormal abdomen and liver morphology). On the other hand, the variants p.(P1007A) and p.(S954L) were significantly associated with later age at diagnosis (p < 0.001) and mildly elevated biomarker levels (p ≤ 0.002), consistent with the juvenile/adult form of NPC1. In addition, p.(I1061T), p.(S954L), and p.(A1035V) were associated with abnormality of eye movements (vertical supranuclear gaze palsy, p ≤ 0.05). We describe the largest and most heterogenous cohort of NPC1 patients published to date. Our results suggest that besides its utility in variant classification, the biomarker PPCS might serve to indicate disease severity/progression. In addition, we establish new genotype–phenotype relationships for “frequent” NPC1 variants.

Analysis of exome data in a UK cohort of 603 patients with syndromic orofacial clefting identifies causal molecular pathways.

Orofacial cleft (OC) is a common congenital anomaly in humans, which has lifelong implications for affected individuals. This disorder can be classified as syndromic or non-syndromic depending on the presence or absence of additional physical or neurodevelopmental abnormalities, respectively. Non-syndromic cleft is often non-familial in nature and has a complex aetiology, whereas syndromic forms tend to be monogenic. Although individual OC-related syndromes have been frequently described in the medical literature, there has not been a comprehensive review across syndromes, thereby leaving a gap in our knowledge, which this paper aims to address. Six hundred and three patients with cleft-related human phenotype ontology terms were identified within the Deciphering Developmental Disorders study. Genes carrying pathogenic/likely pathogenic variants were identified and reviewed enabling a diagnostic yield of 36.5%. In total, 124 candidate genes for syndromic OC were identified, including 34 new genes that should be considered for inclusion in clinical clefting panels. Functional enrichment and gene expression analyses identified three key processes that were significantly overrepresented in syndromic OC gene lists: embryonic morphogenesis, protein stability and chromatin organization. Comparison with non-syndromic OC gene networks led us to propose that chromatin remodelling specifically contributes to the aetiology of syndromic OC. Disease-driven gene discovery is a valid approach to gene identification and curation of gene panels. Through this approach, we have started to unravel common molecular pathways contributing to syndromic orofacial clefting.

PhenoBERT: A Combined Deep Learning Method for Automated Recognition of Human Phenotype Ontology.

Automated recognition of Human Phenotype Ontology (HPO) terms from clinical texts is of significant interest to the field of clinical data mining. In this study, we develop a combined deep learning method named PhenoBERT for this purpose. PhenoBERT uses BERT, currently the state-of-the-art NLP model, as its core model for evaluating whether a clinically relevant text segment (CTS) could be represented by an HPO term. However, to avoid unnecessary comparison of a CTS with each of ∼14,000 HPO terms using BERT, we introduce a two-levels CNN module consisting of a series of CNN models organized at two levels in PhenoBERT. For a given CTS, the CNN module produces only a short list of candidate HPO terms for BERT to evaluate, significantly improving the computational efficiency. In addition, BERT is able to assign an ancestor HPO term to a CTS when recognition of the direct HPO term is not successful, mimicking the process of HPO term assignment by human. In two benchmarks, PhenoBERT outperforms four traditional dictionary-based methods and two recently developed deep learning-based methods in two benchmark tests, and its advantage is more obvious when the recognition task is more challenging. As such, PhenoBERT is of great use for assisting in the mining of clinical text data.

The impact of 22q11.2 copy-number variants on human traits in the general population

While extensively studied in clinical cohorts, the phenotypic consequences of 22q11.2 copy-number variants (CNVs) in the general population remain understudied. To address this gap, we performed a phenome-wide association scan in 405,324 unrelated UK Biobank (UKBB) participants by using CNV calls from genotyping array. We mapped 236 Human Phenotype Ontology terms linked to any of the 90 genes encompassed by the region to 170UKBB traits and assessed the association between these traits and the copy-number state of 504 genotyping array probes in the region. We found significant associations for eight continuous and nine binary traits associated under different models (duplication-only, deletion-only, U-shape, and mirror models). The causal effect of the expression level of 22q11.2 genes on associated traits was assessed through transcriptome-wide Mendelian randomization (TWMR), revealing that increased expression of ARVCF increased BMI. Similarly, increased DGCR6 expression causally reduced mean platelet volume, in line with the corresponding CNV effect. Furthermore, cross-trait multivariable Mendelian randomization (MVMR) suggested a predominant role of genuine (horizontal) pleiotropy in the CNV region. Our findings show that within the general population, 22q11.2 CNVs are associated with traits previously linked to genes in the region, and duplications and deletions act upon traits in different fashions. We also showed that gain or loss of distinct segments within 22q11.2 may impact a trait under different association models. Our results have provided new insights to help further the understanding of the complex 22q11.2 region.

Insights into the perinatal phenotype of Kabuki syndrome in infants identified by genome-wide sequencing.

Increasing use of unbiased genomic sequencing in critically ill infants can expand understanding of rare diseases such as Kabuki syndrome (KS). Infants diagnosed with KS through genome-wide sequencing performed during the initial hospitalization underwent retrospective review of medical records. Human phenotype ontology terms used in genomic analysis were aggregated and analyzed. Clinicians were surveyed regarding changes in management and other care changes. Fifteen infants met inclusion criteria. KS was not suspected prior to genomic sequencing. Variants were classified as Pathogenic (n=10) or Likely Pathogenic (n=5) by American College of Medical Genetics and Genomics Guidelines. Fourteen variants were de novo (KMT2D, n=12, KDM6A, n=2). One infant inherited a likely pathogenic variant in KMT2D from an affected father. Frequent findings involved cardiovascular (14/15) and renal (7/15) systems, with palatal defects also identified (6/15). Three infants had non-immune hydrops. No minor anomalies were universally documented; ear anomalies, micrognathia, redundant nuchal skin, and hypoplastic nails were common. Changes in management were reported in 14 infants. Early use of unbiased genome-wide sequencing enabled a molecular diagnosis prior to clinical recognition including infants with atypical or rarely reported features of KS while also expanding the phenotypic spectrum of this rare disorder.

Next generation phenotyping with quantitative narration for DEGCAGS syndrome.

The diagnosis of rare Mendelian disorders usually relies upon the interpretation of prose and is complicated by a lack of objective, reproducible phenotypic data. To address this limitation, we developed a next generation phenotyping workflow to phenotypically characterize developmental delay with gastrointestinal, cardiovascular, genitourinary, and skeletal abnormalities (DEGCAGS). We identified 15 people affected with DEGCAGS, including one novel patient identified at our hospital and 14 patients previously reported in the literature. Human Phenotype Ontology (HPO) terms were extracted from the patient chart and literature review. The HPO terms were sorted by count according to HPO hierarchy of terms. Phenotypes that cosegregate were identified utilizing a co-occurrence matrix. A quantitative narrative illustrated by violin plots was created for our patient from phenotypic data per each day of hospital admission. A total of 252 unique HPO terms were extracted from the patient record and literature review. The highest count of systemically sorted and unsorted terms and the most commonly co-occurring terms were described. A violin plot of phenotype occurrences demonstrated a progression of phenotypes over time. NGP offers a quantitative approach to phenotyping to generate phenotypic data in an objective and reproducible manner akin to NGS.

IMPROVE-DD: Integrating multiple phenotype resources optimizes variant evaluation in genetically determined developmental disorders.

Diagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs.

Termviewer - A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation.

Clinical notes from electronic health records (EHRs) contain a large amount of clinical phenotype data on patients that can provide insights into the phenotypic presentation of various diseases. A number of Natural Language Processing (NLP) algorithms have been utilized in the past few years to annotate medical concepts, such as Human Phenotype Ontology (HPO) terms, from clinical notes. However, efficient use of NLP algorithms requires the use of high-quality clinical notes with phenotype descriptions, and erroneous annotations often exist in results from these NLP algorithms. Manual review by human experts is often needed to compile the correct phenotype information on individual patients. Here we develop TermViewer, a web application that allows multi-party collaborative annotation and quality assessment of clinical notes that have already been processed and tagged by NLP algorithms. TermViewer allows users to view clinical notes with HPO terms highlighted, and to easily classify high-quality notes and revise incorrect tagging of HPO terms. Currently, TermViewer combines MetaMap and cTAKES, two of the most widely used NLP tools for tagging medical terms, and identifies where these two tools agree and disagree, allowing users to perform collaborative manual reviews of computationally generated HPO annotations. TermViewer can be a stand-alone tool for analyzing notes or become part of a machine-learning pipeline where tagged HPO terms can be used as additional input data. TermViewer is available at https://github.com/WGLab/TermViewer.

Diagnosis of a Single-Nucleotide Variant in Whole-Exome Sequencing Data for Patients With Inherited Diseases: Machine Learning Study Using Artificial Intelligence Variant Prioritization

Background In recent years, thanks to the rapid development of next-generation sequencing (NGS) technology, an entire human genome can be sequenced in a short period. As a result, NGS technology is now being widely introduced into clinical diagnosis practice, especially for diagnosis of hereditary disorders. Although the exome data of single-nucleotide variant (SNV) can be generated using these approaches, processing the DNA sequence data of a patient requires multiple tools and complex bioinformatics pipelines. Objective This study aims to assist physicians to automatically interpret the genetic variation information generated by NGS in a short period. To determine the true causal variants of a patient with genetic disease, currently, physicians often need to view numerous features on every variant manually and search for literature in different databases to understand the effect of genetic variation. Methods We constructed a machine learning model for predicting disease-causing variants in exome data. We collected sequencing data from whole-exome sequencing (WES) and gene panel as training set, and then integrated variant annotations from multiple genetic databases for model training. The model built ranked SNVs and output the most possible disease-causing candidates. For model testing, we collected WES data from 108 patients with rare genetic disorders in National Taiwan University Hospital. We applied sequencing data and phenotypic information automatically extracted by a keyword extraction tool from patient’s electronic medical records into our machine learning model. Results We succeeded in locating 92.5% (124/134) of the causative variant in the top 10 ranking list among an average of 741 candidate variants per person after filtering. AI Variant Prioritizer was able to assign the target gene to the top rank for around 61.1% (66/108) of the patients, followed by Variant Prioritizer, which assigned it for 44.4% (48/108) of the patients. The cumulative rank result revealed that our AI Variant Prioritizer has the highest accuracy at ranks 1, 5, 10, and 20. It also shows that AI Variant Prioritizer presents better performance than other tools. After adopting the Human Phenotype Ontology (HPO) terms by looking up the databases, the top 10 ranking list can be increased to 93.5% (101/108). Conclusions We successfully applied sequencing data from WES and free-text phenotypic information of patient’s disease automatically extracted by the keyword extraction tool for model training and testing. By interpreting our model, we identified which features of variants are important. Besides, we achieved a satisfactory result on finding the target variant in our testing data set. After adopting the HPO terms by looking up the databases, the top 10 ranking list can be increased to 93.5% (101/108). The performance of the model is similar to that of manual analysis, and it has been used to help National Taiwan University Hospital with a genetic diagnosis.

The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants.

Electronic health record (EHR) data linked to DNA biobanks are a valuable resource for understanding the phenotypic effects of human genetic variation. We previously developed the phenotype risk score (PheRS) as an approach to quantify the extent to which a patient's clinical features resemble a given Mendelian disease. Using PheRS, we have uncovered novel associations between Mendelian disease-like phenotypes and rare genetic variants, and identified patients who may have undiagnosed Mendelian disease. Although the PheRS approach is conceptually simple, it involves multiple mapping steps and was previously only available as custom scripts, limiting the approach's usability. Thus, we developed the phers R package, a complete and user-friendly set of functions and maps for performing a PheRS-based analysis on linked clinical and genetic data. The package includes up-to-date maps between EHR-based phenotypes (i.e. ICD codes and phecodes), human phenotype ontology terms and Mendelian diseases. Starting with occurrences of ICD codes, the package enables the user to calculate PheRSs, validate the scores using case-control analyses, and perform genetic association analyses. By increasing PheRS's transparency and usability, the phers R package will help improve our understanding of the relationships between rare genetic variants and clinically meaningful human phenotypes. The phers R package is free and open-source and available on CRAN and at https://phers.hugheylab.org. Supplementary data are available at Bioinformatics online.

Neutrophil Transcriptional Deregulation by the Periodontal Pathogen Fusobacterium nucleatum in Gastric Cancer: A Bioinformatic Study.

Background Infection with the periodontal pathogen Fusobacterium nucleatum (F. nucleatum) has been associated with gastric cancer. The present study is aimed at uncovering the putative biological mechanisms underlying effects of F. nucleatum–mediated neutrophil transcriptional deregulation in gastric cancer. Materials and Methods A gene expression dataset pertaining to F. nucleatum-infected human neutrophils was utilized to identify differentially expressed genes (DEGs) using the GEO2R tool. Candidate genes associated with gastric cancer were sourced from the “Candidate Cancer Gene Database” (CCGD). Overlapping genes among these were identified as link genes. Functional profiling of the link genes was performed using “g:Profiler” tool to identify enriched Gene Ontology (GO) terms, pathways, miRNAs, transcription factors, and human phenotype ontology terms. Protein-protein interaction (PPI) network was constructed for the link genes using the “STRING” tool, hub nodes were identified as key candidate genes, and functionally enriched terms were determined. Results The gene expression dataset GEO20151 was downloaded, and 589 DEGs were identified through differential analysis. 886 candidate gastric cancer genes were identified in the CGGD database. Among these, 36 overlapping genes were identified as the link genes. Enriched GO terms included molecular function “enzyme building,” biological process “protein folding,'” cellular components related to membrane-bound organelles, transcription factors ER71 and Sp1, miRNAs miR580 and miR155, and several human phenotype ontology terms including squamous epithelium of esophagus. The PPI network contained 36 nodes and 53 edges, where the top nodes included PH4 and CANX, and functional terms related to intracellular membrane trafficking were enriched. Conclusion F nucleatum-induced neutrophil transcriptional activation may be implicated in gastric cancer via several candidate genes including DNAJB1, EHD1, IER2, CANX, and PH4B. Functional analysis revealed membrane-bound organelle dysfunction, intracellular trafficking, transcription factors ER71 and Sp1, and miRNAs miR580 and miR155 as other candidate mechanisms, which should be investigated in experimental studies.

KBG syndrome: videoconferencing and use of artificial intelligence driven facial phenotyping in 25 new patients

Genetic variants in Ankyrin Repeat Domain 11 (ANKRD11) and deletions in 16q24.3 are known to cause KBG syndrome, a rare syndrome associated with craniofacial, intellectual, and neurobehavioral anomalies. We report 25 unpublished individuals from 22 families with molecularly confirmed diagnoses. Twelve individuals have de novo variants, three have inherited variants, and one is inherited from a parent with low-level mosaicism. The mode of inheritance was unknown for nine individuals. Twenty are truncating variants, and the remaining five are missense (three of which are found in one family). We present a protocol emphasizing the use of videoconference and artificial intelligence (AI) in collecting and analyzing data for this rare syndrome. A single clinician interviewed 25 individuals throughout eight countries. Participants’ medical records were reviewed, and data was uploaded to the Human Disease Gene website using Human Phenotype Ontology (HPO) terms. Photos of the participants were analyzed by the GestaltMatcher and DeepGestalt, Face2Gene platform (FDNA Inc, USA) algorithms. Within our cohort, common traits included short stature, macrodontia, anteverted nares, wide nasal bridge, wide nasal base, thick eyebrows, synophrys and hypertelorism. Behavioral issues and global developmental delays were widely present. Neurologic abnormalities including seizures and/or EEG abnormalities were common (44%), suggesting that early detection and seizure prophylaxis could be an important point of intervention. Almost a quarter (24%) were diagnosed with attention deficit hyperactivity disorder and 28% were diagnosed with autism spectrum disorder. Based on the data, we provide a set of recommendations regarding diagnostic and treatment approaches for KBG syndrome.

Phenotypic Variation in Two Siblings Affected with Shwachman-Diamond Syndrome: The Use of Expert Variant Interpreter (eVai) Suggests Clinical Relevance of a Variant in the KMT2A Gene.

Introduction. Shwachman-Diamond Syndrome (SDS) is an autosomal-recessive disorder characterized by neutropenia, pancreatic exocrine insufficiency, skeletal dysplasia, and an increased risk for leukemic transformation. Biallelic mutations in the SBDS gene have been found in about 90% of patients. The clinical spectrum of SDS in patients is wide, and variability has been noticed between different patients, siblings, and even within the same patient over time. Herein, we present two SDS siblings (UPN42 and UPN43) carrying the same SBDS mutations and showing relevant differences in their phenotypic presentation. Study aim. We attempted to understand whether other germline variants, in addition to SBDS, could explain some of the clinical variability noticed between the siblings. Methods. Whole-exome sequencing (WES) was performed. Human Phenotype Ontology (HPO) terms were defined for each patient, and the WES data were analyzed using the eVai and DIVAs platforms. Results. In UPN43, we found and confirmed, using Sanger sequencing, a novel de novo variant (c.10663G > A, p.Gly3555Ser) in the KMT2A gene that is associated with autosomal-dominant Wiedemann–Steiner Syndrome. The variant is classified as pathogenic according to different in silico prediction tools. Interestingly, it was found to be related to some of the HPO terms that describe UPN43. Conclusions. We postulate that the KMT2A variant found in UPN43 has a concomitant and co-occurring clinical effect, in addition to SBDS mutation. This dual molecular effect, supported by in silico prediction, could help to understand some of the clinical variations found among the siblings. In the future, these new data are likely to be useful for personalized medicine and therapy for selected cases.

STXBP1 Stop-Loss Mutation Associated with Complex Early Onset Movement Disorder without Epilepsy.

STXBP1 Stop-Loss Mutation Associated with Complex Early Onset Movement Disorder without Epilepsy.

Allelic overload and its clinical modifier effect in Bardet-Biedl syndrome

Bardet–Biedl syndrome (BBS) is an autosomal recessive ciliopathy characterized by extensive inter- and intra-familial variability, in which oligogenic interactions have been also reported. Our main goal is to elucidate the role of mutational load in the clinical variability of BBS. A cohort of 99 patients from 77 different families with biallelic pathogenic variants in a BBS-associated gene was retrospectively recruited. Human Phenotype Ontology terms were used in the annotation of clinical symptoms. The mutational load in 39 BBS-related genes was studied in index cases using different molecular and next-generation sequencing (NGS) approaches. Candidate allele combinations were analysed using the in silico tools ORVAL and DiGePred. After clinical annotation, 76 out of the 99 cases a priori fulfilled established criteria for diagnosis of BBS or BBS-like. BBS1 alleles, found in 42% of families, were the most represented in our cohort. An increased mutational load was excluded in 41% of the index cases (22/54). Oligogenic inheritance was suspected in 52% of the screened families (23/45), being 40 tested by means of NGS data and 5 only by traditional methods. Together, ORVAL and DiGePred platforms predicted an oligogenic effect in 44% of the triallelic families (10/23). Intrafamilial variable severity could be clinically confirmed in six of the families. Our findings show that the presence of more than two alleles in BBS-associated genes correlated in six families with a more severe phenotype and associated with specific findings, highlighting the role of the mutational load in the management of BBS cases.

Genes and Diseases: Insights from Transcriptomics Studies.

Results of expression studies can be useful to clarify the genotype-phenotype relationship. However, according to data from recent literature, there is a large group of genes that are revealed as differentially expressed (DE) in many studies, regardless of the biological context. Additional analyses could shed more light on the relationships between genes, their differential expression, and diseases. We generated a set of 9972 disease genes from five gene-phenotype databases (OMIM, ORPHANET, DDG2P, DisGeNet and MalaCards) and a report of the International Union of Immunological Societies. To study transcriptomics of disease and non-disease genes in healthy tissues, we obtained data from the Human Protein Atlas (HPA) website. We analyzed the dependency between expression in healthy tissues and gene occurrence in Gene Expression Omnibus series using tools within the Enrichr libraries. The results of expression studies were annotated with Gene Ontology (GO) and Human Phenotype Ontology (HPO) terms. Using transcriptomics analysis of healthy tissues, we validated the previous findings of higher expression levels of disease genes in pathologically linked tissues compared to other tissues. Preferentially DE genes were generally highly expressed in one or multiple tissues and were enriched for disease genes. According to the results of GO enrichment analyses, both down- and up-regulated DE genes most often took part in immune response, translation and tissue-specific processes. A connection between DE-related pathology and the diversity of HPO terms was found. Investigating a link between expression and phenotype contributes to understanding the mode of development and progression of human diseases.

Abstract LB501: Making discoveries with Kids First Variant DataBase and WorkBench

Abstract The Gabriella Miller Kids First Pediatric Research Program (Kids First) aims at facilitating researchers to uncover new insights into the biology of childhood cancer (CC) and structural birth defects (SBD), including the discovery of shared genetic pathways between these disorders. Kids First has two initiatives, which are whole genome sequencing (WGS) of biospecimens from families with CC/SBD, and establishing Kids First Data Resource. Kids First Data Resource Center (KFDRC) developed Kids First Data Resource Portal (KFDRP; https://portal.kidsfirstdrc.org/), which is a centralized platform to search, view, analyze, and identify currently accessible data from both Kids First and collaborative cohorts, incorporating omics and phenotypic information of 30 studies and 26,300 participants. Recently KFDRC released two new KFDRP components named Variant DataBase (VDB) and Variant WorkBench (VWB), enabling users to query, mangle, analyze and visualize germline genomic variants. The current release includes ~309 million unique variants in a matrix of more than 61.3 billion individual-chromosomal position occurrences from over 11,500 participants in 17 studies. While VDB provides a quick variant summary, VWB supports scripting languages such as Python, Spark, SQL, R, and MarkDown as in-depth analysis tools enabled by Apache Zeppelin notebooks. In addition to variant calls and phenotypic information such as Human Phenotype Ontology (HPO) terms and Mondo IDs, VWB hosts rich external variant annotations in the public domain, such as Cancer Hotspots, ClinVar, COSMIC, dbNSFP, gnomAD, TOPMed, as well as gene-phenotype links provided by OMIM, HPO, Orphanet, and the Deciphering Developmental Disorders Project. Users can also load additional databases within a notebook (such as the subscription-based Human Gene Mutation Database [HGMD]), import custom datasets as temporary query tables, export analysis outputs to local drives, visualize analysis results in multiple chart styles, display local figures, and save notebooks for sharing, further use and Cavatica projects. In an effort to screen tier 1 genes (n=578) from the most recent Cancer Gene Census provided by COSMIC for rare deleterious variants in all six currently available CC cohorts in KFDRP, we identified over 1.2 million germline variants that are with minor allele frequency no more than 0.00001 among gnomAD/TOPMed datasets and with ratio of number of “damaging” predictions over number of all predictions in dbNSFP between 0.5 and 1, or that are already cataloged in the most recent version of ClinVar/HGMD. The whole process took less than two hours which is much faster than conventional methods. These variant tools in KFDRP enable efficient genomic variant analysis in cancer research with advanced big data technology. Citation Format: Yiran Guo, Jeremy Costanza, Christophe Botek, Miguel Brown, David Higgins, Yuankun Zhu, Bailey Farrow, Allison Heath, Adam Resnick, Vincent Ferretti, Kids First Data Resource Center. Making discoveries with Kids First Variant DataBase and WorkBench [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB501.

Creation and evaluation of full-text literature-derived, feature-weighted disease models of genetically determined developmental disorders.

There are >2500 different genetically determined developmental disorders (DD), which, as a group, show very high levels of both locus and allelic heterogeneity. This has led to the wide-spread use of evidence-based filtering of genome-wide sequence data as a diagnostic tool in DD. Determining whether the association of a filtered variant at a specific locus is a plausible explanation of the phenotype in the proband is crucial and commonly requires extensive manual literature review by both clinical scientists and clinicians. Access to a database of weighted clinical features extracted from rigorously curated literature would increase the efficiency of this process and facilitate the development of robust phenotypic similarity metrics. However, given the large and rapidly increasing volume of published information, conventional biocuration approaches are becoming impractical. Here, we present a scalable, automated method for the extraction of categorical phenotypic descriptors from the full-text literature. Papers identified through literature review were downloaded and parsed using the Cadmus custom retrieval package. Human Phenotype Ontology terms were extracted using MetaMap, with 76–84% precision and 65–73% recall. Mean terms per paper increased from 9 in title + abstract, to 68 using full text. We demonstrate that these literature-derived disease models plausibly reflect true disease expressivity more accurately than widely used manually curated models, through comparison with prospectively gathered data from the Deciphering Developmental Disorders study. The area under the curve for receiver operating characteristic (ROC) curves increased by 5–10% through the use of literature-derived models. This work shows that scalable automated literature curation increases performance and adds weight to the need for this strategy to be integrated into informatic variant analysis pipelines. Database URL: https://doi.org/10.1093/database/baac038