All Publications

  • Resolving SLC6A1 variable expressivity with deep clinical phenotyping and Drosophila models. HGG advances Jay, K. L., Gogate, N., Hall, P. I., Ezell, K. M., Andrews, J. C., Jangam, S. V., Pan, H., Pham, K., German, R., Gomez, V., Jellinek-Russo, E., Storch, E. A., Yamamoto, S., Kanca, O., Bellen, H. J., Dierick, H. A., Cogan, J. D., Phillips, J. A., Hamid, R., Cassini, T., Rives, L., Pruthi, S., Chen, H. C., Posey, J. E., Wangler, M. F. 2026; 7 (1): 100541

    Abstract

    Variants in SLC6A1 result in a rare neurodevelopmental disorder characterized by a variable clinical presentation of symptoms including developmental delay, epilepsy, motor dysfunction, and autism spectrum disorder. SLC6A1 haploinsufficiency has been confirmed as the predominant pathway of SLC6A1-related neurodevelopmental disorder (SLC6A1-NDD); however, the molecular mechanism underlying the variable clinical presentation remains unclear. Here, through work of the Undiagnosed Diseases Network, we identify an individual with an inherited p.A334S variant of uncertain significance. To resolve this variant and better understand the variable expressivity associated with SLC6A1, we assess the phenotypes of the proband in comparison with a cohort of 13 individuals diagnosed with SLC6A1-NDD. We then create an allelic series in Drosophila melanogaster to functionally characterize these variants. Informatic clustering based on these clinical findings points to significant clinical overlap between the unsolved individual and confirmed SLC6A1-NDD. We confirm phenotypes in flies expressing SLC6A1 variants consistent with a partial loss-of-function mechanism. We conclude that the p.A334S variant is a hypomorphic allele and begin to elucidate the underlying variability in SLC6A1-NDD. These insights will inform clinical diagnosis, prognosis, intervention, and inform therapeutic design for those living with SLC6A1-NDD.

    View details for DOI 10.1016/j.xhgg.2025.100541

    View details for PubMedID 41174879

    View details for PubMedCentralID PMC12681531

  • GREGoR: accelerating genomics for rare diseases. Nature Dawood, M., Heavner, B., Wheeler, M. M., Ungar, R. A., LoTempio, J., Wiel, L., Berger, S., Bernstein, J. A., Chong, J. X., Délot, E. C., Eichler, E. E., Lupski, J. R., Shojaie, A., Talkowski, M. E., Wagner, A. H., Wei, C. L., Wellington, C., Wheeler, M. T., Carvalho, C. M., Gibbs, R. A., Gifford, C. A., May, S., Miller, D. E., Rehm, H. L., Samocha, K. E., Sedlazeck, F. J., Vilain, E., O'Donnell-Luria, A., Posey, J. E., Chadwick, L. H., Bamshad, M. J., Montgomery, S. B. 2025; 647 (8089): 331-342

    Abstract

    Rare diseases are collectively common, affecting approximately 1 in 20 individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in next-generation sequencing, development of new computational and functional genomics approaches to prioritize genes and variants and increased global sharing of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Furthermore, all data generated, currently representing over 7,500 individuals from over 3,000 families, are rapidly made available to researchers worldwide through the Analysis, Visualization and Informatics Lab-space (AnVIL) to catalyse global efforts to develop approaches for genetic diagnoses in rare diseases. Most of these families have undergone previous clinical genetic testing but remained unsolved, with most being exome-negative. Here we describe the collaborative research framework, datasets and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.

    View details for DOI 10.1038/s41586-025-09613-8

    View details for PubMedID 41224980

    View details for PubMedCentralID 9119004

  • Improving automated deep phenotyping through large language models using retrieval-augmented generation. Genome medicine Garcia, B. T., Westerfield, L., Yelemali, P., Gogate, N., Rivera-Munoz, E. A., Du, H., Dawood, M., Jolly, A., Lupski, J. R., Posey, J. E. 2025; 17 (1): 91

    Abstract

    Diagnosing rare genetic disorders relies on precise phenotypic and genotypic analysis, with the Human Phenotype Ontology (HPO) providing a standardized language for capturing clinical phenotypes. Rule-based HPO extraction tools use concept recognition to automatically identify phenotypes, but they often struggle with incomplete phenotype assignment, requiring significant manual review. While large language models (LLMs) hold promise for more context-driven phenotype extraction, they are prone to errors and "hallucinations," making them less reliable without further refinement. We present RAG-HPO, a Python-based tool that leverages retrieval-augmented generation (RAG) to elevate accuracy of HPO term assignment by LLM. This approach bypasses the limitations of baseline models and eliminates the need for time- and resource-intensive fine-tuning. RAG-HPO integrates a dynamic vector database, containing > 54,000 phenotypic phrases mapped to HPO IDs, which allows real-time retrieval and contextual matching. The RAG-HPO workflow begins by extracting phenotypic phrases from clinical text via an LLM and then matching them via semantic similarity to entries within the database. The best term matches are returned to the LLM as context for final HPO term assignment of each phrase.Performance was benchmarked on 112 published case reports with 1792 manually assigned HPO terms and compared to Doc2HPO, ClinPhen, and FastHPOCR. In evaluations, RAG-HPO + LLaMa-3.1 70B achieved a mean precision of 0.81, recall of 0.76, and an F1 score of 0.78-significantly surpassing conventional tools (p < 0.00001). RAG-HPO returned 1648 terms, of which 19.1% (315) were false positives that did not exactly match our manually annotated standard. Among these, < 1% (1/315) represented hallucinations, and 1.3% (4/315) represented terms with no ontological relationship to the desired target; the remaining false positives (95.2%, 300/315) were broader ancestor terms of the target term, which may still be relevant to users in many contexts.RAG-HPO is a user-friendly, adaptable tool designed for secure evaluation of clinical text and outperforms standard HPO-matching tools in precision, recall, and F1. Its enhanced precision and recall represent a substantial advancement in phenotypic analysis, accelerating the identification of genetic mechanisms underlying rare diseases and driving progress in genetic research and clinical genomics. RAG-HPO is available at https://github.com/PoseyPod/RAG-HPO .

    View details for DOI 10.1186/s13073-025-01521-w

    View details for PubMedID 40826123

    View details for PubMedCentralID PMC12359922

  • Domain specific phenotypic expansion associated with variants in MACF1. medRxiv : the preprint server for health sciences Gogate, N., Jolly, A., Rosenfeld, J. A., Bahena-Carbajal, P., Bernstein, J. A., Bonner, D., Busa, T., Cristian, I., D'Souza, P., Friedman, J., Gorokhova, S., Haaf, T., Herman, I., Isin, U. U., Jhangiani, S. N., Johnson, I., Lenberg, J., Macnamara, E. F., Maroofian, R., Redlich, O. L., Tifft, C., Tos, T., Vona, B., Zambrano, R. M., Wentzensen, I. M., Wigby, K., Pehlivan, D., Gibbs, R. A., Lupski, J. R., Posey, J. E. 2025

    Abstract

    While heterozygous de novo missense variants in the microtubule-binding GAR domain of Microtubule-actin cross-linking factor 1 (MACF1) cause Lissencephaly 9 with Complex Brainstem Malformations [MIM #618325], the phenotypic impact of variants outside this domain remains unclear.Through collaborative efforts, we assembled a cohort of 10 affected individuals from 8 unrelated families with either biallelic or monoallelic non-GAR domain MACF1 variants who exhibit partially overlapping yet unique phenotypic traits. Combined with previously reported cases, we analyzed genotype and phenotype data from 29 individuals using Human Phenotype Ontology (HPO)-based unsupervised hierarchical clustering.Clustering revealed two distinct phenotypic signatures, suggesting domain-specific effects. Variants outside the GAR domain associate with broader neurodevelopmental phenotypes and variable craniofacial and skeletal expressivity. Additionally, enrichment analysis (p < 0.001) using OMIM HPO sets supported these findings. In contrast to the GAR domain's strong correlation with lissencephaly and brainstem malformations, biallelic non-GAR domain MACF1 variants were linked to diverse developmental anomalies.These results expand the phenotypic spectrum of MACF1-related disorders and highlight the relevance of domain-specific variant effects. Comprehensive genetic and phenotypic assessments are essential for understanding the role of MACF1 in development, informing diagnosis, and guiding future research on cytoskeletal regulation in neurodevelopment.

    View details for DOI 10.1101/2025.06.26.25330137

    View details for PubMedID 40666329

    View details for PubMedCentralID PMC12262753

  • Considerations for reporting variants in novel candidate genes identified during clinical genomic testing. Genetics in medicine : official journal of the American College of Medical Genetics Chong, J. X., Berger, S. I., Baxter, S., Smith, E., Xiao, C., Calame, D. G., Hawley, M. H., Rivera-Munoz, E. A., DiTroia, S., Bamshad, M. J., Rehm, H. L. 2024: 101199

    Abstract

    Since the first novel gene discovery for a Mendelian condition was made via exome sequencing (ES), the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare disease. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery which should in turn increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints, and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks like Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

    View details for DOI 10.1016/j.gim.2024.101199

    View details for PubMedID 38944749

  • Beyond the exome: What's next in diagnostic testing for Mendelian conditions. American journal of human genetics Wojcik, M. H., Reuter, C. M., Marwaha, S., Mahmoud, M., Duyzend, M. H., Barseghyan, H., Yuan, B., Boone, P. M., Groopman, E. E., Délot, E. C., Jain, D., Sanchis-Juan, A., Starita, L. M., Talkowski, M., Montgomery, S. B., Bamshad, M. J., Chong, J. X., Wheeler, M. T., Berger, S. I., O'Donnell-Luria, A., Sedlazeck, F. J., Miller, D. E. 2023; 110 (8): 1229-1248

    Abstract

    Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order, and emerging technologies, such as optical genome mapping and long-read DNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to research consortia focused on elucidating the underlying cause of rare unsolved genetic disorders.

    View details for DOI 10.1016/j.ajhg.2023.06.009

    View details for PubMedID 37541186

  • Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model. Glycobiology Lyman, D. F., Bell, A., Black, A., Dingerdissen, H., Cauley, E., Gogate, N., Liu, D., Joseph, A., Kahsay, R., Crichton, D. J., Mehta, A., Mazumder, R. 2022; 32 (10): 855-870

    Abstract

    Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve the understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hamper their use in research and clinical application. Mass spectrometry measures of 50 N-glycans on 7 serum proteins in liver disease were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized United States Food and Drug Administration-supported BioCompute Object. Using the biomarker data model allows the capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers; it can integrate N-glycan biomarker data with multi-source biomedical data and can foster discovery and insight within a unified data framework for glycan biomarker representation, thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

    View details for DOI 10.1093/glycob/cwac046

    View details for PubMedID 35925813

    View details for PubMedCentralID PMC9487899

  • Patterns of psychopathology and cognition in sex chromosome aneuploidy. Journal of neurodevelopmental disorders Rau, S., Whitman, E. T., Schauder, K., Gogate, N., Lee, N. R., Kenworthy, L., Raznahan, A. 2021; 13 (1): 61

    Abstract

    Sex chromosome aneuploidies (SCAs) are a collectively common family of genetic disorders that increase the risk for neuropsychiatric and cognitive impairment. Beyond being important medical disorders in their own right, SCAs also offer a unique naturally occurring model for studying X- and Y-chromosome influences on the human brain. However, it remains unclear if (i) different SCAs are associated with different profiles of psychopathology and (ii) the notable interindividual variation in psychopathology is related to co-occurring variation in cognitive ability.We examined scores for 11 dimensions of psychopathology [Child/Adult Behavior Checklist (CBCL)] and general cognitive ability [full-scale IQ (FSIQ) from Wechsler tests] in 110 youth with varying SCAs (XXY = 41, XYY = 22, XXX = 27, XXYY = 20) and 131 typically developing controls (XX = 59, XY = 72).All SCAs were associated with elevated CBCL scores across several dimensions of psychopathology (two-sample t tests comparing the euploidic and aneuploidic groups [all |T| > 9, and p < 0.001]). Social and attentional functioning were particularly sensitive to the carriage of a supernumerary Y-chromosome. In particular, the XYY group evidenced significantly more social problems than both extra-X groups (Cohen's d effect size > 0.5, Bonferroni corrected p < .05). There was marked variability in CBCL scores within each SCA group, which generally correlated negatively with IQ, but most strongly so for social and attentional difficulties (standardized β, - 0.3). These correlations showed subtle differences as a function of the SCA group and CBCL scale.There is domain-specific variation in psychopathology across SCA groups and domain-specific correlation between psychopathology and IQ within SCAs. These findings (i) help to tailor clinical assessment of this common and impactful family of genetic disorders and (ii) suggest that dosage abnormalities of X- and Y-linked genes impart somewhat distinct profiles of neuropsychiatric risk.

    View details for DOI 10.1186/s11689-021-09407-9

    View details for PubMedID 34911436

    View details for PubMedCentralID PMC8903493

  • COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model. Briefings in bioinformatics Gogate, N., Lyman, D., Bell, A., Cauley, E., Crandall, K. A., Joseph, A., Kahsay, R., Natale, D. A., Schriml, L. M., Sen, S., Mazumder, R. 2021; 22 (6)

    Abstract

    In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors-compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.

    View details for DOI 10.1093/bib/bbab191

    View details for PubMedID 34015823

    View details for PubMedCentralID PMC8195003

  • Influence of hydrological factors on bacterial community structure in a tropical monsoonal estuary in India. Environmental science and pollution research international Parvathi, A., Catena, M., Jasna, V., Phadke, N., Gogate, N. 2021; 28 (36): 50579-50592

    Abstract

    In the present study, we analyzed variations in bacterial community structure along a salinity gradient in a tropical monsoonal estuary (Cochin estuary [CE]), on the southwest coast of India, using Illumina next-generation sequencing (NGS). Water samples were collected from eight different locations thrice a year to assess the variability in the bacterial community structure and to determine the physico-chemical factors influencing the bacterial diversity. Proteobacteria was the most dominant phyla in the estuary followed by Bacteroidetes, Cyanobacteria, Actinobacteria, and Firmicutes. Statistical analysis indicated significant variations in bacterial communities between freshwater and mesohaline and euryhaline regions, as well as between the monsoon (wet) and nonmonsoon (dry) periods. The abundance of Betaproteobacteria was higher in the freshwater regions, while Alphaproteobacteria and Epsilonproteobactera were more abundant in mesohaline and euryhaline regions of the estuary. Gammaproteobacteria was more abundant in regions with high nutrient concentrations. Various bacterial genera indicating the presence of fecal contamination and eutrophication were detected. Corrplot based on Pearson correlation analysis demonstrated the important physico-chemical variables (temperature, salinity, dissolved oxygen, and inorganic nutrients) that influence the distribution of dominant phyla, class, and genera. The observed spatio-temporal variations in bacterial community structure in the CE were governed by regional variations in anthropogenic inputs and seasonal variations in monsoonal rainfall and tidal influx.

    View details for DOI 10.1007/s11356-021-14263-0

    View details for PubMedID 33963997

  • OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data. JCO clinical cancer informatics Dingerdissen, H. M., Bastian, F., Vijay-Shanker, K., Robinson-Rechavi, M., Bell, A., Gogate, N., Gupta, S., Holmes, E., Kahsay, R., Keeney, J., Kincaid, H., King, C. H., Liu, D., Crichton, D. J., Mazumder, R. 2020; 4: 210-220

    Abstract

    The purpose of OncoMX1 knowledgebase development was to integrate cancer biomarker and relevant data types into a meta-portal, enabling the research of cancer biomarkers side by side with other pertinent multidimensional data types.Cancer mutation, cancer differential expression, cancer expression specificity, healthy gene expression from human and mouse, literature mining for cancer mutation and cancer expression, and biomarker data were integrated, unified by relevant biomedical ontologies, and subjected to rule-based automated quality control before ingestion into the database.OncoMX provides integrated data encompassing more than 1,000 unique biomarker entries (939 from the Early Detection Research Network [EDRN] and 96 from the US Food and Drug Administration) mapped to 20,576 genes that have either mutation or differential expression in cancer. Sentences reporting mutation or differential expression in cancer were extracted from more than 40,000 publications, and healthy gene expression data with samples mapped to organs are available for both human genes and their mouse orthologs.OncoMX has prioritized user feedback as a means of guiding development priorities. By mapping to and integrating data from several cancer genomics resources, it is hoped that OncoMX will foster a dynamic engagement between bioinformaticians and cancer biomarker researchers. This engagement should culminate in a community resource that substantially improves the ability and efficiency of exploring cancer biomarker data and related multidimensional data.

    View details for DOI 10.1200/CCI.19.00117

    View details for PubMedID 32142370

    View details for PubMedCentralID PMC7101249

  • A Targeted Next Generation Sequencing Panel for Non-syndromic Early Onset Severe Obesity and Identification of Novel Likely -Pathogenic Variants in the MC4R and LEP Genes. Indian journal of pediatrics Khadilkar, V., Gogate, N., Gangodkar, P., Ranade, S., Lohiya, N., Ragte, T., Patil, K., Pilankar, A., Anand, S., Bapat, A., Agarwal, M., Kaushik, K., Lote-Oke, R., Khadilkar, A., Phadke, N. 2020; 87 (2): 105-110

    Abstract

    To screen for variants in the MC4R and LEP genes in 46 patients with clinical suspicion of non-syndromic early onset severe obesity (NEOSO).Children with early onset obesity satisfying WHO criteria of obesity were studied. The MC4R and LEP genes were sequenced using a PCR amplicon based NGS on Illumina MiSeq next generation sequencer using an in-house developed protocol.Of the 46 children tested, four were found to have novel pathogenic/likely-pathogenic variants (one in the MC4R gene and three in the LEP gene). In three out of the 4 families, the presence of the variants was confirmed using standard bidirectional capillary sequencing in the probands.Four children with novel likely pathogenic variants in the MC4R and LEP genes are reported. Genetic analysis is crucial in children with early onset obesity and should be considered.

    View details for DOI 10.1007/s12098-019-03129-6

    View details for PubMedID 31925720