Publications by authors named "Lisa Mahanta"

Efforts to implement and evaluate genome sequencing (GS) as a screening tool for newborns and infants are expanding worldwide. The first iteration of the BabySeq Project (2015-2019), a randomized controlled trial of newborn sequencing, produced novel evidence on medical, behavioral, and economic outcomes. The second iteration of BabySeq, which began participant recruitment in January 2023, examines GS outcomes in a larger, more diverse cohort of more than 500 infants up to one year of age recruited from pediatric clinics at several sites across the United States.

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Objective: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups.

Results: Precise SNP tracking showed no sample swap errors within the clinical testing laboratories.

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Objective: Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups.

Results: Precise SNP tracking showed no sample swap errors within the clinical testing laboratories.

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The Mass General Brigham Biobank (formerly Partners HealthCare Biobank) is a large repository of biospecimens and data linked to extensive electronic health record data and survey data. Its objective is to support and enable translational research focused on genomic, environmental, biomarker and family history associations with disease phenotypes. The Biobank has enrolled more than 135,000 participants, generated genomic data on more than 65,000 of its participants, distributed approximately 153,000 biospecimens, and served close to 450 institutional studies with biospecimens or data.

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Article Synopsis
  • The clinical genomics knowledgebase is constantly evolving, which poses challenges for clinical labs due to limited resources for reassessing genetic variants.
  • In a study, automated notifications were sent to clinical sites when variants were reclassified, leading to a summary of the reclassifications and a framework to identify variants likely to change status.
  • Out of 1855 variants reanalyzed, 2% were reclassified, impacting 0.6% of participants, with significant changes mostly due to new evidence, underscoring the necessity of regular updates and automated systems in clinical genetics.
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Over 100 million research participants around the world have had research array-based genotyping (GT) or genome sequencing (GS), but only a small fraction of these have been offered return of actionable genomic findings (gRoR). Between 2017 and 2021, we analyzed genomic results from 36,417 participants in the Mass General Brigham Biobank and offered to confirm and return pathogenic and likely pathogenic variants (PLPVs) in 59 genes. Variant verification prior to participant recontact revealed that GT falsely identified PLPVs in 44.

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Purpose: To evaluate the diagnostic yield and clinical relevance of clinical genome sequencing (cGS) as a first genetic test for patients with suspected monogenic disorders.

Methods: We conducted a prospective randomized study with pediatric and adult patients recruited from genetics clinics at Massachusetts General Hospital who were undergoing planned genetic testing. Participants were randomized into two groups: standard-of-care genetic testing (SOC) only or SOC and cGS.

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Diagnostic laboratories gather phenotypic data through requisition forms, but there is no consensus as to which data are essential for variant interpretation. The ClinGen Cardiomyopathy Variant Curation Expert Panel defined a phenotypic data set for hypertrophic cardiomyopathy (HCM) variant interpretation, with the goal of standardizing requisition forms. Phenotypic data elements listed on requisition forms from nine leading cardiomyopathy testing laboratories were compiled to assess divergence in data collection.

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The rapid worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has propelled the rapid development of serologic tests that can detect anti-SARS-CoV-2 antibodies. These have been used for studying the prevalence and spread of infection in different populations, and helping establish a recent diagnosis of coronavirus disease 2019 (COVID-19), and will likely be used to confirm humoral immunity after infection or vaccination. However, nearly all lab-based high-throughput SARS-CoV-2 serologic assays require a serum sample from venous blood draw, limiting their applications and scalability.

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Although genome sequencing is increasingly available in clinical and research settings, many questions remain about the interpretation of sequencing data. In the MedSeq Project, we explored how much effort is required to evaluate and report on more than 4,500 genes reportedly associated with monogenic conditions, as well as pharmacogenomic (PGx) markers, blood antigen serotyping, and polygenic risk scores in 100 individuals (50 with cardiomyopathy and 50 healthy) randomized to the sequencing arm. We defined the quality thresholds for determining the need for Sanger confirmation.

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There is growing interest in communicating clinically relevant DNA sequence findings to research participants who join projects with a primary research goal other than the clinical return of such results. Since Geisinger's MyCode Community Health Initiative (MyCode) was launched in 2007, more than 200,000 participants have been broadly consented for discovery research. In 2013 the MyCode consent was amended to include a secondary analysis of research genomic sequences that allows for delivery of clinical results.

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Background: Diagnostic testing for genetic cardiomyopathies has undergone dramatic changes in the last decade with next generation sequencing (NGS) expanding the number of genes that can be interrogated simultaneously. Exon resolution copy number analysis is increasingly incorporated into routine diagnostic testing via cytogenomic arrays and more recently via NGS. While NGS is an attractive option for laboratories that have no access to array platforms, its higher false positive rate requires weighing the added cost incurred by orthogonal confirmation against the magnitude of the increase in diagnostic yield.

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The Translational Genomics Core (TGC) at Partners Personalized Medicine (PPM) serves as a fee-for-service core laboratory for Partners Healthcare researchers, providing access to technology platforms and analysis pipelines for genomic, transcriptomic, and epigenomic research projects. The interaction of the TGC with various components of PPM provides it with a unique infrastructure that allows for greater IT and bioinformatics opportunities, such as sample tracking and data analysis. The following article describes some of the unique opportunities available to an academic research core operating within PPM, such the ability to develop analysis pipelines with a dedicated bioinformatics team and maintain a flexible Laboratory Information Management System (LIMS) with the support of an internal IT team, as well as the operational challenges encountered to respond to emerging technologies, diverse investigator needs, and high staff turnover.

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The Biobank and Translational Genomics core at Partners Personalized Medicine requires robust software and hardware. This Information Technology (IT) infrastructure enables the storage and transfer of large amounts of data, drives efficiencies in the laboratory, maintains data integrity from the time of consent to the time that genomic data is distributed for research, and enables the management of complex genetic data. Here, we describe the functional components of the research IT infrastructure at Partners Personalized Medicine and how they integrate with existing clinical and research systems, review some of the ways in which this IT infrastructure maintains data integrity and security, and discuss some of the challenges inherent to building and maintaining such infrastructure.

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Academic medical centers require many interconnected systems to fully support genetic testing processes. We provide an overview of the end-to-end support that has been established surrounding a genetic testing laboratory within our environment, including both laboratory and clinician facing infrastructure. We explain key functions that we have found useful in the supporting systems.

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Purpose: To develop and validate VisCap, a software program targeted to clinical laboratories for inference and visualization of germ-line copy-number variants (CNVs) from targeted next-generation sequencing data.

Methods: VisCap calculates the fraction of overall sequence coverage assigned to genomic intervals and computes log2 ratios of these values to the median of reference samples profiled using the same test configuration. Candidate CNVs are called when log2 ratios exceed user-defined thresholds.

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Pathogenic variants at the DFNB1 locus encompassing the GJB2 and GJB6 genes account for 50% of autosomal-recessive, congenital nonsyndromic hearing loss in the United States. Most cases are caused by sequence variants within the GJB2 gene, but a significant number of DFNB1 patients carry a large deletion (GJB6-D13S1830) in trans with a GJB2 variant. This deletion lies upstream of GJB2 and was shown to reduce GJB2 expression by disrupting unidentified regulatory elements.

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Next-generation sequencing (NGS) technologies have revolutionized genetic testing by enabling simultaneous analysis of unprecedented numbers of genes. However, genes with high-sequence homology pose challenges to current NGS technologies. Because diagnostic sequencing is moving toward exome analysis, knowledge of these homologous genes is essential to avoid false positive and negative results.

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Purpose: Dilated cardiomyopathy is characterized by substantial locus, allelic, and clinical heterogeneity that necessitates testing of many genes across clinically overlapping diseases. Few studies have sequenced sufficient individuals; thus, the contributions of individual genes and the pathogenic variant spectrum are still poorly defined. We analyzed 766 dilated cardiomyopathy patients tested over 5 years in our molecular diagnostics laboratory.

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